An Indexed Bibliography of Genetic Algorithms in Machine Learning

An Indexed Bibliography of Genetic Algorithms in Machine Learning compiled by

Jarmo T. Alander Department of Electrical Engineering and Automation University of Vaasa P.O. Box 700, FIN-65101 Vaasa, Finland phone: +358-6-324 8444, fax: +358-6-324 8467

Dedicated to The Cyber Rodent Robot Group

Report Series No. 94-1-ML (DRAFT 2007/11/02 09:31 )

available via anonymous ftp: site ftp.uwasa.fi directory cs/report94-1 file gaMLbib.ps or file gaMLbib.pdf

c 1994-2007 Jarmo T. Alander Copyright Cover image: Robot B9 lost in space

Trademarks Product and company names listed are trademarks or trade names of their respective companies.

Warning While this bibliography has been compiled with the utmost care, the editor takes no responsibility for any errors, missing information, the contents or quality of the references, nor for the usefulness and/or the consequences of their application. The fact that a reference is included in this publication does not imply a recommendation. The use of any of the methods in the references is entirely at the user’s own responsibility. Especially the above warning applies to those references that are marked by trailing ’†’ (or ’*’), which are the ones that the editor has unfortunately not had the opportunity to read. An abstract was available of the references marked with ’*’.

Contents 1 Preface 1.1 Your contributions erroneous or missing? 1.1.1 How to cite this report? . . . . . . 1.2 How to get this report via Internet? . . 1.3 Acknowledgement . . . . . . . . . . . . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

2 Introduction

1 2 2 2 2 4

3 Statistical summaries 3.1 Publication type . . . . . 3.2 Annual distribution . . . . 3.3 Classification . . . . . . . 3.4 Authors . . . . . . . . . . 3.5 Geographical distribution 3.6 Conclusions and future . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

5 5 5 5 7 8 8

4 Indexes 4.1 Books . . . . . . . . . 4.2 Journal articles . . . . 4.3 Theses . . . . . . . . . 4.3.1 PhD theses . . 4.3.2 Master’s theses 4.4 Report series . . . . . 4.5 Patents . . . . . . . . 4.6 Authors . . . . . . . . 4.7 Subject index . . . . . 4.8 Annual index . . . . . 4.9 Geographical index . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

9 9 9 11 11 11 11 12 13 23 30 31

. . . . . . . . . . .

. . . . . . . . . . .

Bibliography

33

Appendixes

85

A Bibliography entry formats

85

i

List of Tables 2.1

Queries used to extract this subbibliography from the source database.

. . . . . . . . . .

4

3.1 3.2 3.3 3.4 3.5

Distribution of publication type. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Annual distribution of contributions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The most popular subjects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The most productive genetic algorithms in machine learning authors. . . . . . . . . . . . The geographical distribution of the authors working on genetic algorithms in machine learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 6 6 7

A.1 Indexed genetic algorithm special bibliographies available online

ii

. . . . . . . . . . . . . .

8 88

Chapter 1

Preface “ Living organism are consummate problem solvers. They exhibit a versatility that puts the best computer programs to shame. ” John H. Holland, [1]

The material of this bibliography has been extracted from the genetic algorithm bibliography [2], which when this report was compiled (November 19, 2007) contained 20128 items and which has been collected from several sources of genetic algorithm literature including Usenet newsgroup comp.ai.genetic and the bibliographies [3, 4, 5, 6]. The following index periodicals and databases have been used systematically • A: International Aerospace Abstracts: Jan. 1995 – Sep. 1998 • ACM: ACM Guide to Computing Literature: 1979 – 1993/4 • BA: Biological Abstracts: July 1996 - Aug. 1998 • CA: Computer Abstracts: Jan. 1993 – Feb. 1995 • CCA: Computer & Control Abstracts: Jan. 1992 – Dec. 1999 (except May -95) • ChA: Chemical Abstracts: Jan. 1997 - Dec. 2000 • CTI: Current Technology Index Jan./Feb. 1993 – Jan./Feb. 1994 • DAI: Dissertation Abstracts International: Vol. 53 No. 1 – Vol. 56 No. 10 (Apr. 1996) • EEA: Electrical & Electronics Abstracts: Jan. 1991 – Apr. 1998 • EI A: The Engineering Index Annual: 1987 – 1992 • EI M: The Engineering Index Monthly: Jan. 1993 – Apr. 1998 (except May 1997) • Esp@cenet patents – Apr. 2002 • IEEE: IEEE and IEE Journals – Fall 2002 • N: Scientific and Technical Aerospace Reports: Jan. 1993 - Dec. 1995 (except Oct. 1995) • NASA NASA ADS www bibliography database: – Dec. 2002 • P: Index to Scientific & Technical Proceedings: Jan. 1986 – Dec 1999 (except Nov. 1994) • PA: Physics Abstracts: Jan. 1997 – June 1999 • PubMed: National Library of Medicine Jan. 2000 – Oct. 2000 • SPIE Web The International Society for Optical Engineering – June 2002 1

2

1.1

Genetic algorithms in machine learning

Your contributions erroneous or missing?

The bibliography database is updated on a regular basis and certainly contains many errors and inconsistences. The editor would be glad to hear from any reader who notices any errors, missing information, articles etc. In the future a more complete version of this bibliography will be prepared for the genetic algorithms in machine learning research community and others who are interested in this rapidly growing area of genetic algorithms. When submitting updates to the database, paper copies of already published contributions are preferred. Paper copies (or ftp ones) are needed mainly for indexing. We are also doing reviews of different aspects and applications of GAs where we need as complete as possible collection of GA papers. Please, do not forget to include complete bibliographical information: copy also proceedings volume title pages, journal table of contents pages, etc. Observe that there exists several versions of each subbibliography, therefore the reference numbers are not unique and should not be used alone in communication, use the key appearing as the last item of the reference entry instead. Complete bibliographical information is really helpful for those who want to find your contribution in their libraries. If your paper was worth writing and publishing it is certainly worth to be referenced right in a bibliographical database read daily by GA researchers, both newcomers and established ones. For further instructions and information see ftp.uwasa.fi/cs/GAbib/README.

1.1.1

How to cite this report?

The complete BiBTEX record for this report is shown below:

You can also use the BiBTEX file GASUB.bib, which is available in our ftp site ftp.uwasa.fi in directory cs/report94-1 and contains records for all GA subbibliographies.

1.2

How to get this report via Internet?

Versions of this bibliography are available via anonymous ftp or www from the following site: media ftp ftp

country Finland Finland

site ftp.uwasa.fi ftp.uwasa.fi

directory /cs/report94-1 /cs/report94-1

file gaMLbib.ps(.Z)

Observe that these versions may be somewhat different and perhaps reduced as compared to this volume that you are now reading. Due to technical problems in transforming LATEXdocuments into html ones the www versions contain usually less information than the corresponding ftp ones. It is also possible that the www version is completely unreachable. The directory also contains some other indexed GA bibliographies shown in table A.1. In case you do not find a proper one please let us know: it may be easy to tailor a new one.

1.3

Acknowledgement

The editor wants to acknowledge all who have kindly supplied references, papers and other information on genetic algorithms in machine learning literature. At least the following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Helio J. C. Barbosa, Hans-Georg Beyer, Christian Bierwirth, Peter Bober Joachim Born, Ralf Bruns, I. L. Bukatova, Thomas B¨ ack, Chhandra Chakraborti, Nirupam Chakraborti, David E. Clark, Carlos A. Coello Coello, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, J. Wayland Eheart, Bogdan Filipiˇc, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina GorgesSchleuter, Hitoshi Hemmi, Vasant Honavar, Jeffrey Horn, Aristides T. Hatjimihail, Heikki Hyötyniemi

Acknowledgement

3

Mark J. Jakiela, Richard S. Judson, Bryant A. Julstrom, Charles L. Karr, Akihiko Konagaya, Aaron Konstam, John R. Koza, Kristinn Kristinsson, Malay K. Kundu, D. P. Kwok, Jouni Lampinen, Jorma Laurikkala, Gregory Levitin, Carlos B. Lucasius, Timo Mantere, Michael de la Maza, John R. McDonnell, J. J. Merelo, Laurence D. Merkle, Zbigniew Michalewics, Melanie Mitchell, David J. Nettleton, Volker Nissen, Ari Nissinen, Tatsuya Niwa, Tomasz Ostrowski, Kihong Park, Jakub Podgórski, Timo Poranen, Nicholas J. Radcliffe, Colin R. Reeves, Gordon Roberts, David Rogers, David Romero, Sam Sandqvist, Ivan Santib´ an ˜ez-Koref, Marc Schoenauer, Markus Schwehm, Hans-Paul Schwefel, Michael T. Semertzidis, Davil L. Shealy, Moshe Sipper, William M. Spears, Donald S. Szarkowicz, El-Ghazali Talbi, Masahiro Tanaka, Leigh Tesfatsion, Peter M. Todd, Marco Tomassini, Andrew L. Tuson, Kanji Ueda, Jari Vaario, Gilles Venturini, Hans-Michael Voigt, Roger L. Wainwright, D. Eric Walters, James F. Whidborne, Stefan Wiegand, Steward W. Wilson, Xin Yao, Xiaodong Yin, and Ljudmila A. Zinchenko. The editor also wants to acknowledge Elizabeth Heap-Talvela for her kind proofreading of the manuscript of this bibliography and Tea Ollanketo and Sakari Kauvosaari for updating the database. Prof. Timo Salmi and the Computer Centre of University of Vaasa is acknowledged for providing and managing the online ftp site ftp.uwasa.fi, where these indexed bibliographies are located.

Chapter 2

Introduction “Many scientist, possibly most scientist, just do science without thinking too much about it. They run experiments, make observations, show how certain data conflict with more general views, set out theories, and so on. Periodically, however, some of us—scientists included—step back and look at what is going on in science.” David L., Hull, [7]

The table 2.1 gives the queries that have been used to extract this bibliography. The query system as well as the indexing tools used to compile this report from the BiBTEX-database [8] have been implemented by the author mainly as sets of simple awk and gawk programs [9, 10]. string learning Machine Learning Machine Learning classifier system bucket brigade

field ANNOTE JOURNAL BOOKTITLE ANNOTE ANNOTE

class Machine learning Machine Learning Journal Machine Learning Book/Proceedings Classifier systems Classifier systems

Table 2.1: Queries used to extract this subbibliography from the source database.

4

Chapter 3

Statistical summaries type book section of a book part of a collection journal article proceedings article report PhD thesis MSc thesis others total

This chapter gives some general statistical summaries of genetic algorithms in machine learning literature. More detailed indexes can be found in the next chapter. References to each class (c.f table 2.1) are listed below: • Classifier systems 198 references ([11]-[208]) • Machine Learning Book/Proceedings 55 references ([209]-[263]) • Machine Learning Journal 28 references ([264]-[291])

number of items 9 5 37 231 532 39 31 3 7 894

Table 3.1: Distribution of publication type.

• Machine learning 612 references ([292][903]) Observe that each reference is included (by the computer) only to one of the above classes (see the queries for classification in table 2.1; the textual order in the query gives priority for classes).

3.1

The annual distribution is also shown in fig. 3.1. The average annual growth of GA papers has been approximately 40 % during late 70’s - early 90’s.

Publication type

This bibliography contains published contributions including reports and patents. All unpublished manuscripts have been omitted unless accepted for publication. In addition theses, PhD, MSc etc., are also included whether or not published somewhere. Table 3.1 gives the distribution of publication type of the whole bibliography. Observe that the number of journal articles may also include articles published or to be published in unknown forums.

3.2

3.3

Annual distribution

Classification

Every bibliography item has been given at least one describing keyword or classification by the editor of this bibliography. Keywords occurring most are shown in table 3.3.

Table 3.2 gives the number of genetic algorithms in machine learning papers published annually. 5

6


year 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 total

items 1 0 0 0 0 0 0 2 3 13 13 33 42 54 92 87 44 29 8 8 1

year 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

items 0 0 0 0 0 0 1 0 2 9 22 39 57 93 117 62 36 17 6 3 894

Table 3.2: Annual distribution of contributions.

6Genetic algorithms in machine learning ccc c ccc c c 1000 c c c number of papers c c (log scale) c cc 100 cc ssss cc s ss c ss sss c c s c s s c cc s s c cc cccc 10 s ss cc c s c c c c cc s s c c c s s 1c cc s s s1960 2007/11/02

1970

1980 YEAR

1990

2000

Figure 3.1: The number of papers applying genetic algorithms in machine learning (•, N = 897 ) and total GA papers (◦, N = 20128 ). Observe that the last few years are most incomplete in the database.

Total machine learning classifier systems neural networks learning genetic programming robotics control rule based systems coevolution agents pattern recognition fuzzy systems image processing engineering classifiers scheduling comparison classification games parallel GA medicine review hybrid evolution economics controllers expert systems bucket brigade analysing GA spectroscopy rules implementation signal processing optimization inductive learning databases autonomous agents artificial life population size patent manufacturing game theory fuzzy logic diagnosis chemistry automata ALECSYS others

722 509 192 124 42 42 40 31 26 25 24 21 21 18 18 18 17 17 16 15 14 14 13 13 13 13 13 10 9 9 8 7 7 6 6 6 6 6 6 5 5 5 5 5 5 5 5 5 1773

Table 3.3: The most popular subjects.

Authors

3.4

7

Authors

Table 3.4 gives the most productive authors.

total number of authors Fogarty, Terence C. Goldberg, David E. Grefenstette, John J. Jong, Kenneth A. De Holland, John H. Dorigo, Marco Wilson, Stewart W. Carse, Brian Forrest, Stephanie Pollack, Jordan B. Bull, Lawrence Furuhashi, Takeshi Liepins, Gunar E. Schultz, Alan C. Spears, William M. Hilliard, M. R. Iba, Hitoshi Riolo, Rick L. Venturini, Gilles Booker, Lashon B. Garis, Hugo de Giordana, A. Herrera, Francisco Lin, Chin-Teng Schwefel, Hans-Paul Sklar, Elizabeth Uchikawa, Yoshiki Zhao, Qiangfu Bala, Jerzy W. Ellis, David I. Gordon, Diana F. Horn, Jeffrey Juillé, Hugues Kakazu, Yukinori Nakaoka, K. Nordin, Peter Reynolds, Robert G. Saitta, L. Smith, Stephen F. Tamburino, Louis A. Vafaie, Haleh Yao, Xin 32 authors 56 authors 151 authors 887 authors

1168 20 20 20 17 15 14 14 11 10 10 8 8 8 8 8 7 7 7 7 6 6 6 6 6 6 6 6 6 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 3 2 1

Table 3.4: The most productive genetic algorithms in machine learning authors.

8

3.5


Geographical distribution

Table 3.5 gives the geographical distribution of authors, when the country of the author was known. Over 80% of the references of the GA source database are classified by country. 2007/11/02

country Total United States Japan United Kingdom Germany Italy China France Spain Australia Finland Taiwan Canada South Korea Belgium The Netherlands Austria Sweden India Mexico Poland Others

special n % 829 100.00 308 37.15 108 13.03 86 10.37 49 5.91 39 4.70 30 3.62 23 2.77 22 2.65 20 2.41 19 2.29 16 1.93 13 1.57 10 1.21 9 1.09 9 1.09 6 0.72 6 0.72 5 0.60 4 0.48 4 0.48 40 4.80

comparison δ[%] ∆[%] +9.30 +0.43 +0.03 −1.16 +1.85 −1.13 +0.24 +0.98 −0.03 −1.18 −0.16 +0.03 −1.13 +0.25 +0.06 +0.11 +0.28 −0.91 −0.05 −0.40 −1.89

+33 +3 +0 −16 +65 −24 +9 +59 −1 −34 −8 +2 −48 +30 +6 +18 +64 −60 −9 −45 −28

all N 18950 5278 2388 1960 1340 540 900 480 316 463 657 397 291 443 159 195 116 84 286 101 167 1266

% 100.00 27.85 12.60 10.34 7.07 2.85 4.75 2.53 1.67 2.44 3.47 2.09 1.54 2.34 0.84 1.03 0.61 0.44 1.51 0.53 0.88 6.69

Table 3.5: The geographical distribution of the authors working on genetic algorithms in machine learning (n) compared (δ and ∆) to all authors in the field of GAs (N ). In the comparison column: δ% = nNT otal %special−%all and ∆ = (1 − N nT otal ) × 100%. ∆ is the relative (%) deviation from the expected number of special papers. Observe that joint papers may have authors from several countries and that not all authors have been attributed to a country.

3.6

Conclusions and future

The editor believes that this bibliography contains references to most genetic algorithms in machine learning contributions upto and including the year 1998 and the editor hopes that this bibliography could give some help to those who are working or planning to work in this rapidly growing area of genetic algorithms.

Chapter 4

Indexes 4.1

Books

AISB Quarterly,

[65]

Analytica Chimica Acta,

The following list contains all items classified as books.

[322, 360, 108]

Annals of Operations Research, APL Quote Quad,

Applied Artificial Intelligence, Adaptive learning by genetic algorithms. Analytical results and applications to economics models. Second edition., [740]

Applied Intelligence,

Anticipatory Learning Classifier Systems,

Artificial Intelligence,

Genetic Algorithms for Machine Learning,

[641]

Artif. Life Robot. (Japan),

[624]

[294, 423, 110, 123]

Artificial Intelligence in Engineering,

[389]

Genetic Learning for Adaptive Image Segmentation,

[430, 153]

[708]

Artif. Intell. Eng. (UK),

[34]

[682]

[70, 90]

[378]

Artificial Intelligence in Medicine, Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms, [367]

Artificial Life,

Induction: Processes of Inference, Learning, and Discovery,

Bioinformatics,

[823]

[416] [675]

Bull. Fac. Eng. Univ. Tokushima (Japan),

Learning Algorithms: Theory and Applications in Signal Processing, Control and Communications, [631] [143]

Chin. J. Comput. (China),

[658]

Chinese Journal of Advanced Software Research, Communications of the ACM,

total 9 books

Journal articles

Complex Systems,

[775, 858, 189]

Complexity (USA),

[597]

Comput. Econ.,

[436]

Computer,

[408] [701]

Computer Methods in Applied Mechanics and Engineering, [24]

Computer Physics Communications, Computers Chem. (Oxford), Computers & Structures,

[25]

[347]

Decis Support Syst (Netherlands),

Adaptive Behavior,

[349, 856]

Decision Support Systems,

AI Applications,

[830]

Design Theory and Methodology,

AI Communications,

[53, 517]

Egypt. Comput. J. (Egypt),

AI Mag.,

[656]

Electronics Letters,

9

[39, 442]

[31]

Cybernetics and Systems, [537]

[748]

[98]

Computers & Operations Research,

Acta Electronica Sinica (China),

[564]

[474]

Computer Methods and Programs in Biomedicine,

The following list contains the references to every journal article included in this bibliography. The list is arranged in alphabetical order by the name of the journal. AAAAAAA,

[732]

Central European Journal for Operations Research and Economics, [595]

System Indentification Through Simulated Evolution: A Machine Learning Approach to Modeling, [798]

4.2

[530, 583]

[22]

[492]

Asian Computer Weekly,

Parallelism and Programming in Classifier Systems,

[739]

Artificial Intelligence in Engineering (UK),

[524]

[30]

[350]

[435]

[178]

10


Engineering Applications of Artificial Intelligence, [27, 738]

Int. J. Prod. Res. (UK),

Engineering with Computers,

Int. J. Syst. Sci. (UK), [686]

[807]

European Journal of Operational Research, Evolutionary Computation, Expert Systems,

Intelligent Data Analysis,

[54, 66, 531, 606, 643]

Expert Systems with Applications,

Irish Journal of Psychology,

[681]

[165]

[694]

J. Chin. Inst. Electr. Eng. (Taiwan),

[452, 576] [339, 343,

[11, 171]

[120]

J. am. Soc. Inf. Sci. (USA),

[323]

345, 351]

J. Electr. Eng. Inf. Sci. (Taiwan),

[733]

[693]

J. Inst. Electron. Eng. Korea S (South Korea),

High Technol. Lett. (China),

[611]

J. Inst. Image Electron. Eng. Jpn. (Japan),

IEE Proceedings, Control Theory Appl.,

J. Intell. Fuzzy Syst. (Netherlands),

[575]

J. Intell. Syst. (UK),

[458, 890, 891]

IEEE Robotics & Automation Magazine, IEEE Trans. Neural Netw. (USA),

[754]

[561, 713]

J. Jpn. Soc. Precision Eng. (Japan),

[712]

J. Korea Inst. Telem. Electron. S (South Korea),

[371]

IEEE Transactions of Knowlledge and Data Engineering, [484]

J. Phys. A. Math. Gen. (UK), J. Shanghai Tiedao Univ.,

IEEE Transactions on Computers,

[734]

[377]

[538]

J. Jpn. Soc. Artif. Intell. (Japan),

[325]

[763]

IEEE Transaction on Fuzzy Systems,

[506]

J. Softw. (China),

IEEE Transactions on Evolutionary Computation,

[308,

[614]

[554]

[758]

[716, 731]

Journal of Chemical Information and Computer Sciences,

317, 344]

[335]

IEEE Transactions on Evolutionary Computations, IEEE Transactions on Evolutionary Computing, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Neural Networks,

[372]

[369]

[338, 607, 728]

IEEE Transactions on Industrial Electronics,

[119]

[332, 391, 547, 551,

645, 670, 707, 735, 744]

Journal of Computational Biology,

[552]

Journal of Economic Dynamics & Control, Journal of Evolutionary Economics,

IEEE Transactions on Robotics and Automation,

[690]

IEEE Transactions on Systems, Man and Cybernetics - Part B: Cybernetics, [319] IEEE Transactions on Systems, Man, and Cybernetics, [296, 312, 314, 50, 569, 578, 135]

IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, [536] IEEE/ASME Transactions on Mechatronics,

[307]

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, [392, 406, 201] IEICE Transactions on Fundamentals of Electronics, Communications and Computer Science”, , [709]

Journal of Intelligent & Fuzzy Systems,

[504, 550]

Journal of Machine Learning Research,

[264]

Journal of Theoretical Biology,

[69, 91]

Machine Learning,

[265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291]

Mathematical and Computer Modelling, Metabolomics,

Microprocessing and Microprogramming Journal, [790] Nature,

[657]

[297, 407] [745]

Inf. Sci. (USA),

Nippon Kikai Gakkai Ronbunshu C Hen,

Information Processing letters,

[329]

[35]

Information Processing & Management,

[295]

Neurocomputing (Netherlands), OEGAI-J. (Austria), Pattern Recognition,

[721]

Pattern Recognition Letters,

Information Sciences,

[29]

PC AI (USA), [644]

[628]

[293]

[615]

Personal Computer World, Physica D,

[679]

[37]

Information Science,

Int. J. Approx. Reason. (USA),

[572, 592]

[704]

Pattern Anal. Appl. (UK), [541]

EURO-Micro

[730]

Neural Computat. Appl., Neural Networks,

[688, 736]

[366]

Neurocomputing,

Information and Software Technology,

[197]

Mathematics and Computers in Simulation,

IMA Journal of Mathematics Applied in Business and Industry, [431] [673, 691]

[542]

[828]

Jpn. J. Fuzzy Theory Syst. (USA),

Neural Processing Letters,

[567]

Int. J. Model. Simul. (USA),

[337]

[587, 756, 864]

Journal of Physics A: Mathematical and General,

IEEE Transactions on Pattern Analysis and Machine Intelligence, [574]

IIE Transactions,

[303]

International Journal of Intelligent Systems,

[585]

Genetic Programming and Evolvable Machines,

IEEE Expert,

[761]

Intelligent Systems Engineering,

Expert Systems Applications (UK),

Fuzzy Sets and Systems,

Intell. Data Anal. (Netherlands),

[359, 114]

[60]

Food Science & Technology,

[609]

[854]

[797]

[167]

Theses

11

Proceedings of the IEEE,

University of Michigan, [145, 803, 833, 184]

[320]

Revue d’Intelligence Artificielle,

[459]

Robotics and Autonomous Systems, SIGCSE Bulletin,

[766]

Soft Computing,

[33]

Syst. Comput. Jpn. (USA), The Analyst,

University of North Carolina at Chapel Hill, [299]

University of Oulu,

University of Rochester, [667]

[590]

University of Strathclyde,

[364]

Vanderbilt University,

Transactions of the Institute of Electronics, Information, and Communication Engineers D-II (Japan), [579] Transactions of the Institute of Electronics, Information, and Communication Engineers A (Japan), [589] Transactions of the Institute of Electronics, Information, and Communication Engineers D-II, [700] Transactions of the Institute of Electronics, Information, and Communication Engineers D-II (Japan), [402, Wuhan Univ. J. Nat. Sci. (China),

[526, 539]

4.3.2

Air Force Institute of Technology,

total 3 thesis in 3 schools

4.4

The following two lists contain theses, first PhD theses and then Master’s etc. theses, arranged in alphabetical order by the name of the school.

PhD theses [742]

Catholic University of Nijmegen,

[671]

Michigan State University,

Report series

The following list contains references to all papers published as technical reports. The list is arranged in alphabetical order by the name of the institute. Aarhus University,

[38]

Brandeis University,

[318, 666]

California Institute of Technology,

[89]

Carnegie-Mellon University,

Katholieke Universiteit Nijmegen,

George Mason University,

[102]

[379, 488]

Iowa State University,

[824]

Stanford University,

Limburg University,

[862]

[76, 857]

Los Alamos National Laboratory,

[825]

The University of Michigan,

Naval Research Laboratory,

[441]

[140, 141, 144]

[494]

Navy Center for Applied Research in Artificial Intelligence,

University of Alabama, [180, 207] University of Alberta,

[835]

International Computer Science Institute (ICSI), [791, 792]

[778]

Politechnico di Milano, [793]

The Ohio State University,

[768]

[850]

Georgia Institute of Technology,

[880]

Mississippi State University,

[795]

[635]

University of Tennessee, [168]

Theses

Drexel University,

Master’s theses

This list includes also “Diplomarbeit”, “Tech. Lic. Theses”, etc.

total 231 articles in 146 series

4.3.1

[882]

total 31 thesis in 25 schools

University of Helsinki,

559, 715]

[853]

Wayne State University, [765]

Transactions of the Institute of Electrical Engineera of Japan C, [529]

Brandeis University,

[361]

University of Pittsburgh, [827, 874]

Transactions of the Information Processing Society of Japan, [512]

4.3

[829]

[440, 468]

[840]

Politecnico di Milano,

[134]

University of Cincinnati, [897]

Santa Fe Institute,

[769]

University of Edinburgh, [689]

Slovak Technical University,

University of Granada,

[650]

The Rowland Institute for Science,

University of Helsinki,

[877]

The University of Birmingham,

University of Illinois at Urbana-Champaign,

[396]

[449] [821, 186, 187]

[94, 109]

The University of Texas at Austin,

[490]

12


University of Bonn,

[771]

University of Dortmund, [496, 499] University of Granada,

[500]

University of Illinois at Urbana-Champaign, University of Massachusetts,

[59, 151]

[770]

University of Michigan, [158, 181] University of Turku,

[525]

University of Vaasa,

[208]

Universit¨ at Bremen,

[648]

total 39 reports in 27 institutes

4.5

Patents

The following list contains the names of the patents of genetic algorithms in machine learning. The list is arranged in alphabetical order by the name of the patent. Adaptive computing system cabable of learning and discovery, [822] Computer implemented machine learning and control system, [757] Machine learning procedures for generating image domain feature detectors, [802] Method of controlling a classifier system, Offline teaching methid, [749] total 5 patents

[162]

Authors

4.6

13

Authors

The following list contains all genetic algorithms in machine learning authors and references to their known contributions.

Abbattista, Fabio,

[220, 861]

Aspn¨ as, Anders,

[505]

Bin, Cao Xian,

[731] [502]

Abdalla, M. I. A.,

[435]

Atkin, Marc C.,

[770]

Birk, A.,

Abdelrahman, T.,

[837]

Augier, S.,

[437]

Blair, Alan D.,

Abu Zitar, Raed Abdullah, [765, 818]

Augustsson, P.,

[352]

Abunawass, Adel M.,

[766]

Aukee, Pauliina,

[723]

Abu-Zitar, R.,

[347]

Ackley, David H.,

[680, 269,

724, 727]

Blekas, K.,

[538, 114]

Bloemer, A.,

[841]

Aurand, Jean-Philippe, [42]

Blume, Christian,

[776]

[767]

Aytug, Haldun,

[39, 682]

Bock, P.,

[383]

Adami, C.,

[413]

Babenko, Vladimir,

[675]

Bod´ en, Ida,

[903]

Adami, Chris,

[768]

B¨ ack, Thomas,

[772, 773, 237]

Bohari, Abdul Rahman, [566]

Adams, William,

[503]

Backer, Gerriet,

[508]

Bonarini, Andrea,

[320, 29, 380]

Addis, Tom,

[893]

Baghdadchi, Jalal,

[304]

Bonelli, Pierre,

[777, 121, 122]

[444]

Booker, Lashon B.,

Adeli, H.,

[391]

Baiardi, Fabrizio,

Agui, Takeshi,

[377]

Baker, Philip N.,

Aguilar, J.,

[603, 679]

Bala, Jerzy W.,

Aguilar-Ruiz, Jes´ us S.,

[329]

Aguirre, Arturo Hern´ andez, Aizawa, A. N.,

[484]

Akbarzadeh-T., M.-R.,

[586]

[271, 123,

124, 125, 126, 154]

[321]

Akec, J.,

[630]

Alander, Jarmo T.,

[903, 208]

Aleksander, Igor,

[730]

[366] [471, 531,

606, 234, 235]

Baltes, Jacky,

[342]

Baluja, Shumeet,

[379, 488, 495]

Bandyopadhyay, Sanghamitra, Banzhaf, Wolfgang,

[718]

Borisov, Arkady,

[683]

Borrajo, D.,

[706]

Botta, M.,

[804]

Boughton, E. M.,

[719]

Boullart, L.,

[25]

Bowden, Royce O.,

[567, 778]

Bradbeer, P. V. G.,

[612]

[224, 496,

499, 757]

Barone, Luigi,

[306, 746]

Bradbeer, P.,

[638]

Barreiros, J.,

[752]

Branke, J¨ urgen,

[219]

Barricelli, N. A.,

[828]

Braun, David W.,

[618]

Barry, A. M.,

[120]

Braun, Heinrich,

[623]

Barry, Alwyn,

[20]

Broadhurst, David,

[360, 364, 370]

Brown, A. D.,

[601]

Brown, C. T.,

[413]

Browne, W.,

[106]

Aler, R.,

[706]

Alkadhimi, K.,

[454]

Ambainis, A.,

[604]

Anderson, C. W.,

[291]

Anderson, Charles W.,

[429]

Andina, Diego,

[211]

Andrey, Philippe,

[37]

Bello, G. Lo,

[227, 717]

Bruha, Ivan,

[303, 699]

Angeline, P. J.,

[719]

Benoit,

[295]

Bruhn, P.,

[704]

Anglano, C.,

[227, 717]

Berka, Petr,

[303]

Bruynooghe, M.,

[789]

Brzozowski, D.,

[322]

Buckles, Bill P.,

[321]

Buckley, James J.,

[381]

Buiu, C.,

[688, 736]

Bull, Larry,

[33, 356]

Becker, Bernd,

[497]

Behrendt, S.,

[469]

Belew, Richard K.,

[774, 775,

Browne, William N. L., [27]

270, 276]

Anon.,

[854]

Bernad´ o i Mansilla, Ester,

Aoki, Shinji,

[749]

Bernaschi, M.,

[748]

Aoki, Y.,

[709]

Bersini, Hugues,

[58]

[21, 739]

Araki, K.,

[512]

Bhanu, B.,

[378]

Araki, M.,

[588, 692]

Bharadwaj, K. K.,

[11]

Arentoft, Peter Rolann, [38]

Bhattacharyya, S.,

[743]

Arifovic, Jasmina,

Bianchi, Dario,

[509]

[436, 769]

Bull, Lawrence,

[40, 56, 424,

65, 67, 82, 83, 137]

Bull, L.,

[33, 432]

14


Bullington, Stanley F.,

[567]

Chen, Shu-Heng,

[358]

Corne, D.,

[672]

Bullock, J.,

[106]

Chen, Yung-Yaw,

[445]

Costa, E.,

[752]

Bullock, John,

[27]

Chengjian, Wei,

[565]

Costa, Ernesto,

[646] [625] [85]

Burak, I.,

[695]

Chiang, Chih-Kuan,

[607]

Coutinho, M.S.,

Burks, A. W.,

[822, 162]

Chinniah, C.,

[638]

Cribbs III, H. Brown,

Burks, Christian,

[267]

Chisholm, K. J.,

[612]

Butz, Martin V.,

[23, 28, 34]

Cho, In-Hyun,

[687]

Buydens, Lutgarde M. C.,

[98, 108]

Cho, Sung Bae,

[708]

Callaghan, V.,

[300, 741]

Cho, Sung-Bae,

[584]

Callaghan, Victor,

[325]

Choi, Jin Young,

[543]

Capi, G.,

[353]

Chongistitvatana, P.,

[510]

Caponetti, Laura,

[220]

Chow, C. R.,

[781]

Carapella, G.,

[220]

Chowdhury, M.,

[545]

Carbonaro, Antonella,

[127]

Christensen, Henrik I.,

[368, 372]

Card, H. C.,

[601]

Chu, C. H.,

[781]

Carrick, C.,

[779]

Chu, Lon-Chan,

[396, 484]

Carroll, C. C.,

[149]

Chung, Hung-Yuan,

[607]

Carr-West, M.,

[741]

Chung, Hyun-Soo,

[341]

Chunyi, Shi,

[658]

Cichosz, P.,

[77]

Carse, Brian,

[41, 57, 61, 64, 65, 489, 82, 84, 594, 97, 599]

Caruana, Richard A.,

Casadei, Giorgio,

[127]

Casadio, Rita,

[657]

Castiglione, F.,

[748]

Castro, J. L.,

[819]

Caunite, Inta,

[683]

Cecconi, Federico,

[860]

Cellier, Fran¸cois E.,

[780]

[311]

Chai, R.,

Chan, Z. S. H.,

[350]

Chandler, B., Chang, Shan-Ping,

[335]

Curry, B.,

[359]

Cyliax, Ingo,

[618]

Dai, Dawei,

[526]

Dai, Honghua,

[212]

da Rocha Costa, Antonio Carlos, [462]

Darwen, Paul J.,

[591, 753]

Das, Rajarshi,

[429, 291]

Davidson, C. E.,

[322]

Davis, Lawrence,

[232, 272,

784, 785]

Dawid, Herbert,

[587, 595, [59, 66, 151,

152]

Delgado, M.,

[819]

Clarke, Sarah J.,

[364]

Denecker, M.,

[789]

Cloate, Ian,

[371]

Desper, R.,

[604]

Cobb, H. G.,

[383]

DeStefano, Claudio,

[613]

Cobb, Helen G.,

[440, 782, 246]

Dike, B. A.,

[24]

Cockcroft, Victor,

[505]

Ding, Lan,

[632]

Dodgson, N.,

[116]

Dominic, Stephen,

[429, 291]

Donnart, Jean-Yves,

[43, 569]

Cohen, Paul R.,

[770]

Collard, Philippe,

[42, 68, 128]

Colley, Martin,

[325]

Colley, M.,

[300, 741]

Colmenares, A.,

[603, 679]

Colombetti, Marco,

[423, 791, 792]

Dorigo, Marco,

[198] [101]

Cundari, Thomas R.,

[215]

[390]

Chalk, K.,

[684]

Claridge, Ela,

Coello Coello, Carlos A., [321] Cercone, Nick,

[104]

Cuenca, Cristina,

Deb, Kalyanmoy, Cisnero, Lia N. Tusanotte,

[488, 495]

Crowley, Philip H.,

597, 740]

[256, 257]

Caruana, Rich,

Croix, Edward V. de St., [506]

[605, 732] [869]

Compiani, M.,

[129, 130, 12,

[265, 58, 417, 423, 266, 238, 132, 133, 790, 791, 134, 135, 792, 793]

Dorndorf, Ulrich,

[442, 862]

Downing, K. L.,

[330]

Doya, Eiji Uchibe andf Kenji,

[357,

368, 372]

Char, K. Govinda,

[636]

Doya, K.,

[353]

Chavez, Margarita G.,

[865]

Congdon, Clare Bates,

[441]

Drechsler, Nicole,

[339]

Chen, C. H.,

[697]

Conrad, M.,

[553]

Drechsler, Rolf,

[339, 497]

Chen, Hsinchun,

[694]

Conrad, Michael,

[407, 434, 602]

Ducoulombier, A.,

[229]

Chen, J. R.,

[549, 631]

Cook, Diane J.,

[783]

Dudey, Peter,

[794]

Chen, Jong-Chen,

[698]

Cooling, J. E.,

[384]

Dumeur, Renaud,

[136]

Chen, S.,

[454, 763]

Cord´ on, Oscar,

[338, 511, 644]

Dumitrache, I.,

[688, 736]

131]

Authors

15

Dunn, Warwick B.,

[366]

Durbin, Dennis R.,

[22]

Forrest, Stephanie,

[267, 139, 276, 140, 141, 142, 143, 144, 277, 145]

Forsyth, Richard S., Duvendack, Shannon,

[401]

[241, 242,

243, 244]

Girshgorn, S. L.,

[710]

Glesner, Manfred,

[71]

Glielmo, L.,

[307]

Dwinnell, W.,

[615]

Foth, Kilian A.,

[340]

Glorennec, Pierre Yves, [388, 621]

Dwormann, Garett,

[44]

Franco, Aurali B.,

[865]

Goddard, J.,

[518, 622]

Dzeroski, S.,

[216]

Francone, Frank D.,

[224, 757]

Goerick, Christian,

[316, 519]

Eaton, C.,

[436]

Franke, R.,

[648]

Echizen-Ya, H.,

[512]

Frankowski, Jacek,

[365]

Eick, C. F.,

[513, 568]

Fredriksson, Kimmo,

[635]

Golobardes i Rib´ e, E.,

[739]

Eick, C.,

[472]

Frey, P. W.,

[278]

Gomide, Fernando,

[399]

Eisenstein, Jacob,

[616]

F.Smith, Stephen,

[385]

Gonzalez, Antonio,

[662]

El-Fallah, A.,

[24]

Fu, Zhiwei,

[761]

Gonz´ alez, Antonio,

[728]

Elfwing, Stefan,

[357, 368, 372]

Fujii, Makoto,

[111]

Goodacre, Royston,

Ellis, David I.,

[323, 360,

Fujii, Nobutada,

Endo, S.,

[465, 491]

Endoh, Satoshi,

[86]

[323, 360,

[346]

Goodloe, M.,

[155]

Goodman, Erik D.,

[879]

[334, 456,

668, 799]

Funes, Pablo,

[23, 28, 59, 66, 515, 88, 123, 148, 806, 807, 149, 808, 279, 150, 151, 152, 153, 154, 285, 834]

364, 370]

364, 366, 370]

Fukuda, Toshio,

Goldberg, David E.,

[666, 680, 705]

Gordon, Diana F.,

[422, 468,

494, 275, 892]

Enee, G.,

[115]

Englander, Arnold C.,

[796]

Enhong, Chen,

[611]

Eriksson, A.,

[353]

Escazut, Cathy,

[42, 68, 115]

Eshelman, Larry J.,

[257]

Eskin, Eleazar,

[737]

Esp, D. G.,

[99]

Furuhashi, Takeshi,

[52, 414, 425, 69, 466, 469, 111, 205]

[731]

Fairhurst, M. C.,

[32]

Gracias, N.,

Furuya, Tatsumi,

[521]

Graves, S. J.,

[155]

Gali´ c, Elvis,

[516]

Greene, David Perry,

[385, 809, 280]

Gallego, M. J.,

[665]

Gan, Meng,

[467]

Gao, Xinbo,

[461, 487] [901, 261,

262, 263, 902, 237]

Garrell i Guiu, Josep Maria, [21, 739]

[45, 55, 13]

Garrido, J.,

[619]

Fang, H. L.,

[697]

Gathercole, Chris,

[386, 689]

Farach, M.,

[604]

Gayko, Jens E.,

[309]

Fariselli, Piero,

[657]

Gegout, C.,

[608]

Farmer, J. Doyne,

[797]

Geladi, Paul,

[903]

Fellrath, Paul,

[60]

Gelenbe, E.,

[571]

Fern´ andez, G.,

[450]

Gell-Mann, Murray,

[47, 87]

Ficici, Sevan G.,

[318, 720]

Gero, John S.,

[480, 530, 632]

Filipiˇ c, Bogdan,

[738]

Geyer-Schulz, Andreas, [70, 704]

Finnerty, S.,

[46]

Ghosh, Samiran,

[351]

Fitzpatrick, J. Michael, [281]

Giani, Antonella,

[444]

Floreano, Dario, Fogarty, Terence C.,

[36, 40, 56, 57, 424, 65, 431, 432, 67, 489, 82, 83, 84, 594, 97, 599, 239, 240, 137, 138] [719, 237, 798]

Grefenstette, John J.,

[313, 389, 218, 453, 468, 503, 520, 236, 810, 281, 282, 283, 811, 812, 246, 813, 814, 284, 816, 881]

Grefestette, John J.,

[815]

Große, Daniel,

[339]

Guan, Ling,

[344]

Guangzeng, Feng,

[460]

Gwee, B. H.,

[576]

Hagras, Hani,

[325]

Hagras, H.,

[300, 741]

Hahsler, M.,

[704]

Hajela, Prabhat,

[31, 113]

Halgamuge, Saman K., [71] Hall, L. O.,

[695]

Han, Seung-Soo,

[523, 558]

Hansen, J. V.,

[524]

Harik, Georges R.,

[515]

Hartmann, Uwe,

[48, 204]

Gillies, Andrew McGilvary, [802, 803]

Hasegawa, Yasuhisa,

[334, 456]

Giordana, A.,

Hashimoto, R.,

[700]

Hashimoto, Ryoichi,

[654]

Hashiyama, Tomonori,

[469]

[620, 227,

717, 245, 804, 805]

Giordana, Attilio, Fogel, David B.,

[548]

[486, 91, 673]

Fairley, Andrew,

[297]

[795]

Furuhashi, T.,

Garis, Hugo de, Fa, Wang Xu,

Gordon, Edward O.,

477, 517]

[387, 415,

16


Hassoun, Mohamad H., [818]

Horn, Jeffrey,

[59, 66, 88,

Iyer, A.,

[694]

Jamshidi, Mohammad,

[586]

Janakiraman, Janani,

[876]

Janikow, C. Z.,

[118]

151, 152]

Hatono, Itsuo, Haverinen, Janne, Hayashi, Yoichi, He, Jun, He, Zhenya, Hedges, S., Heiberg, Vilhelm,

[346]

Hosoi, Tomoaki,

[346]

Hosokawa, A.,

[327]

Hosokawa, S.,

[402, 896]

Janikow, Cesary Z.,

[393]

Hu, Cheng,

[716]

Janikow, Cezary Z.,

[286, 829]

Hu, Xiaohua,

[390]

Jean, Kaung-Tsang,

[445]

Huang, Dijia,

[15]

Jeffers, J. N. R.,

[830]

Huang, J.,

[471, 531, 606]

Jensen, Kaj Aage,

[38]

Huang, Shyh-Jier,

[593]

Jiao, Li-Cheng,

[213]

Huimin, Chen,

[537]

Jin, Bingyao,

[760]

Hung, Chuan-Chang,

[593]

Jiˇr´ı, Handl´ıˇr,

[533]

Hung, Shih-Lin,

[391, 825]

Joel, D.,

[349]

Hunter, A.,

[49]

Jong, Kenneth A. De,

H¨ usken, Michael,

[309, 316, 333]

Hwang, Min Woong,

[543]

[361, 292] [381] [464] [760] [49] [627]

Hekanaho, Jukka,

[221, 505,

525, 225]

Hercog, Luis Miramontes,

[36]

Herrera, Francisco,

[338, 500,

501, 511, 644, 819]

Heudin, Jean-Claude,

[684]

Hewahi, N. M.,

[11]

Hibbert, D. B.,

[108]

Higuchi, Tatsuo,

[551, 592]

Higuchi, Tetsuya,

[317, 836]

Higuchi, T.,

[479, 600]

Iba, Hitoshi,

Hilliard, M. R.,

[842, 843,

[302, 573,

600, 629, 691, 721, 725]

[451]

Hirata, Masaya,

[392]

Joung, Chi-Sun,

[734]

Juan, Liu,

[539]

Ichimura, Takumi,

[696]

Juang, Chia-Feng,

[296, 685]

Ichimura, T.,

[528]

Juhola, Martti,

[674, 701, 723]

Juill´ e, Hugues,

[522, 666,

[484]

230, 705, 742]

Ieumwananonthachai, Arthur,

Ho, T. K., Hoffmeister, Frank, H¨ ohfeld, Markus, Holford, Karen M., Holford, K., Holifield, Gregory A.,

[507]

Jun, Gao,

[731]

Jun, Hyo-Byung,

[734]

Jung, Sung Hoon,

[572, 693]

Kadaba, Nagesh,

[249]

[392]

Kadyrov, A. S.,

[678]

Imecs, Maria,

[73]

Kakazu, Y.,

[712]

Inagaki, Yashio,

[392]

Kakazu, Yukinori,

Inglada, Jordi,

[331]

Inoue, M.,

[470, 529]

Ireson, N. S.,

[424, 431, 432]

Isasi, Pedro,

[652]

Isasi, P.,

[706, 117]

Igel, Christian,

[333]

Ikeda, Shiro,

[451]

Ikkai, Y.,

[470, 529]

Ikuno, Yasumasa,

[421] [483] [350] [772] [516] [27] [106] [354]

Holland, John H.,

[123, 158, 247, 14, 821, 822, 248, 159, 823, 160, 161, 285, 162, 163, 164]

Holmes, John H.,

[621]

[820]

Hiraki, Hideaki,

Ho, Chun-Ying,

[314, 686, 735]

Jouffe, Lionel,

[369]

Ieumwananonthachai, A.,

Hirayama, N.,

Jou, Chong-Ping,

Iba, S. Kamio Hitoshi,

844, 250, 169, 845, 171]

Hintz, Kenneth J.,

[217, 422, 453, 471, 531, 606, 273, 233, 274, 786, 787, 234, 235, 788, 236, 237, 275]

[22, 35, 89]

Ishibuchi, Hisao,

[301, 326, 78,

Holyoak, Keith J.,

[823, 160]

Homaifar, Abdollah,

[149]

Ishii, Naohiro,

[669]

Honavar, Vasant,

[634, 824]

Ishii, N.,

[702]

Honda, T.,

[476]

Ishino, Yoko,

[482, 562, 585]

Hondo, Naohiro,

[573]

Ishobuchi, Hsiao,

Honkela, Timo,

[878]

Hoque, M. S., Horihan, Jason W.,

589]

[395, 573,

831, 832, 167]

Kakuyama, T.,

[377]

Kalles, D.,

[210]

Kamani, Sejal,

[446]

Kamiya, A.,

[504]

Kampen, Antoine H. C. van, 102, 108]

Kampfner, R. R.,

[833]

Kaneko, Masakatsu,

[749]

Kaneko, S.,

[476]

Kang, Lishan,

[464]

[119]

Kannan, S.,

[604]

Ito, H.,

[521]

Kanoh, H.,

[327]

[32]

Itoh, H.,

[473]

Kargupta, Hillol,

[351, 664]

[362]

Iwata, M.,

[600]

Kargupta, H.,

[718]

[98,

Authors

17

Karr, Charles L.,

[834, 166]

Kodratoff, Y.,

[437]

Larra˜ naga, Pedro,

[574]

Katai, O.,

[754]

Koehler, Gary J.,

[39]

Larranaga, P.,

[665]

Kataoka, T.,

[51]

Kohlmorgen, Udo,

[219]

Laurikkala, Jorma,

[674, 701, 723]

Kawabata, Hiroaki,

[392]

Kohno, Tadashi,

[799]

Lavine, Barry K.,

[322] [443, 690]

Kawakami, Hiroshi,

[86]

Kohonen, Teuvo,

[745]

Lee, C. S. George,

Kawakami, Takashi,

[167]

Koljonen, Janne,

[903]

Lee, C. S. G.,

[570]

Kawakami, T.,

[712]

Kommu, Venkataramana,

Lee, Chae Y.,

[609]

Kazakov, Vladimir A.,

[530]

Komoda, N.,

[470, 529]

Lee, Dong Wook,

[614, 734]

Keane, Martin A.,

[839]

Kondo, Mitsuhiro,

[231]

Lee, Jongsoo,

[31]

Keesing, R.,

[894]

Koppen, M.,

[642]

Lee, Kuo-Tsai,

[445]

Kel, Alexander,

[675]

Korousic-Seljak, B.,

[384]

Lee, Seong-Yong,

[341]

Kelemen, Arpad,

[73]

Kosugi, Y.,

[559]

Lee, Seung-Ik,

[584]

Lee, S.,

[378]

Lehrach, Hans,

[675]

Kell, Douglas B.,

[366]

Kovacs, Tim,

[837]

[26, 94, 105,

109]

Kennedy, H. C.,

[638]

Koza, John R.,

[433, 726,

Kenny, Louise C.,

[366]

Kiernan, L.,

[165]

Kr´ al´ıik, Pavel,

[699]

Kijima, Kyoichi,

[312]

Kralik, Pavel,

[303]

Kil, R. M.,

[581]

Krawiec, Krzysztof,

[345]

Kil, Rhee M.,

[447]

Krishnamraju, P.,

[381]

Kim, B.,

[113]

Krishnanand, K.,

[552]

Kim, Daijin,

[687]

Krizman, V.,

Kim, G. H.,

[570]

Kim, Gyoung H.,

838, 839]

Leung, Kwong Sak,

[498, 577,

899, 900]

Leung, Kwong Sal,

[416, 426]

Leung, Shu-Chung,

[406]

Levenick, Jim,

[110]

Levitan, B. S.,

[750]

Li, Gang,

[212]

[738]

Li, Wenhua,

[461, 487]

Ku, K. W. C.,

[637]

Li, Wen-Ming,

[483]

[443]

Ku, Kim Wing C.,

[744]

Li, Ying,

[213]

Kim, Gyoung Hwan,

[690]

Kubo, Masao,

[395]

Li, Yong-Cheng,

[507]

Kim, Heung Bum,

[572, 693]

Kubota, Naoyuki,

[668]

Li, Yun,

[545]

Kim, Jung H.,

[310]

Kuijpers, Cindy M. H., [574]

Liangjie, Zhang,

[537]

Kim, Sang-Woon,

[709]

Kumagai, T.,

[700]

Liedtke, C.-E.,

[841]

Kim, Tag Gon,

[572]

Kumagai, Totu,

[654]

Liepins, Gunar E.,

Kim, Y. J.,

[513]

Kumar, Rajeev,

[707]

Kim, Yeong-Joon,

[568]

Kumar, R.,

[649, 762]

Kim, Y.,

[472]

Kumbla, Kishan K.,

[586]

Kimura, H.,

[504]

Kuncicky, David C.,

[397]

Kirlik, A.,

[835]

Kuo, Tzu-Wen,

[358]

Kis, Zoltan,

[73]

Kuri, Angel,

[676, 681]

[842, 843, 844, 250, 169, 845, 170, 171]

Likartsis, A.,

[639]

Likas, A.,

[538]

Lim, M. H.,

[576]

Lima, J. A.,

[548]

Lima, W. C. de,

[625]

Lin, Cheng-Jiang,

[686]

Lin, Cheng-Jian,

[733]

Kitamura, S.,

[624]

Kuriyama, Y.,

[696]

Kitano, Hiroaki,

[836]

Kuscu, Ibrahim,

[534]

Klasa, Stan,

[374]

Kuscu, I.,

[527]

Klebus, G. P.,

[77]

Kvasniˇ cka, Vladim´ır,

[449]

Lin, Jianhua,

[72]

Knight, Leslie,

[448]

Lampinen, Jouni,

[759]

Lin, Jiann-Yow,

[296]

Knight, L.,

[493]

Lan, Hongxiang,

[467]

Lin, Jin-Jye,

[607]

Kobayashi, S.,

[504]

Langley, Pat,

[474]

Lindholm-Sethson, Britta,

Kodjabachian, J´ erˆ ome,

[394]

Lanzi, Pier Luca,

[35]

Linkens, Derek A.,

Lin, Chin-Teng,

[296, 314,

319, 685, 686, 735]

[903]

[575]

18


Littman, Michael L.,

[767]

Masui, Toshiyuki,

[420]

Moore, Carolynne J.,

[27]

Litz, L.,

[703]

Masujima, Y.,

[421]

Moore, C.,

[106]

Liu, Han-Leih,

[319]

Matsumoto, T.,

[713]

Moores, Anthony J.,

[322]

Liu, Y.,

[564]

May, G. S.,

[523, 558]

Morgan, P. H.,

[359]

Liu, Yong,

[317, 464, 670]

Mayfield, Howard T.,

[322]

Mori, S.,

[483]

Liu, Zemin,

[540]

Mayley, G.,

[514, 643]

Moriarty, David E.,

[222, 490, 268]

Llor` a i F` abrega, X.,

[739]

Maza, Michael de la,

[848]

Morikawa, Kojima,

[69, 205]

LoBello, G.,

[620]

McAulay, Alastair D.,

Morss, L.,

[638]

Mottl, M.,

[704]

M¨ uhlenbein, Heinz,

[438, 851]

Mulholland, M.,

[108]

Munetomi, Masaharu,

[579]

[50, 172, 849,

173]

Longshaw, T.,

[655]

McCallum, R. Andrew, [578, 252] Longshaw, Tom, Lopes, Heitor S., Lopez, I., L´ opez, I., Losee, Robert M.,

[640]

McKay, R. I.,

[485]

Mehlmann, A.,

[597]

Mehra, R. K.,

[24]

Mehta, K.,

[743]

[625] [518]

Munetomo, Masaharu , [544]

[622] [541]

Munetomo, Masaharu,

[590]

Munir-ul, M.,

[545]

Munoz, A.,

[398]

Munro, A.,

[97]

Murao, H.,

[624]

Murase, I.,

[476]

Murata, Tadahiko,

[78, 589]

Murga, Roberto H.,

[574]

Muro, Zen-ichirou,

[53]

Muruzabal, Jorge,

[398, 79]

Muselli, Marco,

[852]

Myers, Jenny,

[366]

Myler, Harley R.,

[747]

Meier-Ewert, Sebastian, [675] Louis, S. J., Louis, Sushil John, Lozano, Manuel,

[583]

[563] [324, 660]

Luca P., A. de, Lucas, S. M., Luk, Andrew, Luk, B. L.,

Meng, Chen-Lu,

[30]

Meng, Wu,

[460]

Menzel, Wolfgang,

[340]

[500, 501, 511]

Lu, Yuchang,

Luca, A. D. de P.,

[349]

[641]

Lu, Y. C.,

Lu, Yung-Hsiang,

Meilijson, I.,

Meservy, R. D.,

[524]

Meyer, Jean-Arcady,

[43, 394, 569]

Miagkikh, V. V.,

[751]

Michalski, R. S.,

[850]

Michelena, M. J.,

[665]

Middleton, L. T.,

[174]

[362] [518] [622] [557] [406] [763]

Miikkulainen, Risto,

[298, 222,

Lynch, Douglas B.,

[397]

MacLennan, Bruce J.,

[847]

Mikami, Sadayoshi,

[654, 832]

Nade, T.,

[421]

MacLeod, K.,

[779]

Mikami, S.,

[700]

Nagahara, Toshikuni,

[392]

Macready, W. G.,

[750]

Miller, Geoffrey F.,

[800, 855]

Nagahashi, Hiroshi,

[377] [377]

490, 268]

Maeda, H.,

[69, 91]

Miller, John H.,

[141, 142]

Nagao, Tomoharu,

Maffione, Angelo,

[861]

Miller, Julian F.,

[722]

Nagasaka, Ichiro,

[103]

Magdalena, Luis,

[450, 475]

Mitchell, Melanie,

[277]

Nagayoshi, M.,

[421]

Mak, M. W.,

[637]

Mitchell, Tom M.,

[656]

Nakamiti, Gilberto,

[399]

Mak, Man Wai,

[744]

Mitl¨ ohner, Johann,

[90]

Nakamura, Taro,

[310]

Maletic, Jonathan I.,

[869]

Miyata, Y.,

[673]

Nakano, Kaoru,

[451]

Mammone, Richard J.,

[409]

Miyata, Yujiro,

[414]

Nakaoka, K.,

[52, 414, 69,

466, 91]

Manderick, Bernard,

[251]

Mizuno, Naoki,

[566]

Maniezzo, Vittorio,

[403]

Molina, J. M.,

[117]

Marcelli, Angelo,

[610, 613]

Momouchi, Y.,

[512]

Marks, Robert E.,

[863, 864]

Monasterio, F.,

[450, 475]

Nambiar, Raghu,

[549, 631]

Mars, Phil,

[549, 631]

Montanari, D.,

[129, 12, 131]

Negoita, Mircea Gh.,

[400]

Mart´ınez, Aleix M.,

[293]

Moon, Beoung-Xu,

[341]

Nehmzow, U.,

[348]

Nakaoka, N., Nakashima, Tomoharu,

[425] [301, 326, 78,

119]

Authors

19

Neri, Filippo,

[209, 477, 517]

Ono, Noriko,

[315]

Pesch, Erwin,

[442, 862]

Neri, F.,

[617]

Oommen, B. John,

[506]

Petrovski, I.,

[216]

Neves, J.,

[74]

Orvosh, David,

[785]

Picaza, J. M.,

[665] [331]

Ng, Sin-Chun,

[406]

Ouchi, Yuji,

[661]

Pigeon, Luc,

Ngam, H. W.,

[350]

Ozyurt, I. B.,

[546]

Pipe, A. G.,

[653]

Nickolay, B.,

[642]

Pachowicz, Peter W.,

[234, 235]

Pipe, Anthony G.,

[560]

Nii, M.,

[326]

Packard, Norman H.,

[797, 858]

Piramuthu, S.,

[609]

Nijo, Tetsuo,

[315]

Pakath, Ramakrishnan, [30]

Polani, Daniel,

[298]

Nikolaev, N. I.,

[226]

Palareti, Aldopaolo,

Poli, Riccardo,

[677]

Polichroniadis, T.,

[116]

Nikolopoulos, Chris,

[401, 60, 62]

Nisbett, Richard E.,

[823, 160]

Nishio, Y.,

[605, 732]

Niv, Y.,

[349]

Nolfi, Stefano,

[860]

Nordin, Peter,

[352, 224,

496, 499, 757]

Nordling, Torbj¨ orn E. M.,

[903]

[127]

Palmer, Mark R.,

[844, 250,

169, 845]

Pollack, Jordan B.,

Pan, J. S.,

[697]

Pan, Zhengjun,

[464]

Panayides, N.,

[174]

Papagelis, A.,

[210]

[318, 522, 666, 230, 705, 269, 711, 720, 724, 753]

Paredis, Jan,

[478, 492, 859]

Parisi, Domenico,

[860]

Polvichai, J.,

[510]

Pomeranz, Irith,

[837]

Pop, Horia F.,

[340]

Porto, V. W.,

[719]

Posp´ıchal, Jiˇr´ı,

[449]

Potter, Mitchell A.,

[453, 846]

Poza, Mikel,

[574]

Ptitsyn, Andrey,

[675]

Puch, W. F.,

[751]

Putro, Utomo Sarjono,

[312]

Nuseirat, A. M. A.,

[347]

Park, Kyu Ho,

[572, 693]

Nygard, Kendall E.,

[249]

Park, Yongjoo,

[342]

Nyongesa, H. Okola,

[575]

Parker, Gary B.,

[299, 618]

Nystr¨ om, Josefina,

[903]

Parodi, Alexandre,

[777, 121, 122]

Parrazales, R. U.,

[518]

Qingsheng, Cai,

[611]

Parsons, Rebecca J.,

[267]

Qingyi, Wang,

[611]

Patel, Mukesh J.,

[403, 417, 196]

Qiu, Jiancheng,

[633]

Pattichis, C. S.,

[174]

Quinlan, J. R.,

[287]

R, Rajani, Joshi,

[552]

Obata, K.,

[486]

Ochi, M.,

[588, 692]

Odetayo, Michael O.,

[112, 853]

Oechtering, Peter,

[651]

Oh, Jae Chan,

[50, 172, 849,

173]

Pattichis, Constantinos S.,

[547]

Oh, Sang-Hoon,

[581]

Patton, Ronald,

[747]

Rad, A. B.,

[350]

Ohira, T.,

[481]

Pedrycz, Witold,

[404, 452]

Radi, A.,

[677]

Ohkawa, T.,

[470, 529]

Pei, Wenjiang,

[760]

Raedt, L. De,

[789]

Ohm, P.,

[438]

Pelikan, Martin,

[367]

Ragg, Thomas,

[336, 623]

Ohuchi, A.,

[465, 491]

Pelik´ an, Martin,

[449]

Rahardja, S.,

[576]

[861]

Rahmani, A. T.,

[481]

Ohuchi, Azuma,

[86]

Pelillo, Marcello,

Ohwi, J.,

[75]

Pe˜ na-Reyes, Carlos Andr´ es,

Okamoto, J.,

[402]

Peng, Hui,

[214]

Ramadan, Z.,

[108]

Okamoto, T.,

[713]

Penttinen, Jorma,

[723]

Ramos, Isabel,

[329]

Oliveira, M.,

[752]

Pereira, F.,

[752]

Ramsey, Connie Loggia, [283]

Oliver, Jim R.,

[175]

Pereira, Francisco,

[646]

Rangarajan, G.,

[250, 169]

Olsson, Bj¨ orn,

[228]

Pereira, H.,

[548]

Ranson, Aaron L.,

[865]

Omata, T.,

[714]

Perelson, Alan S.,

[797]

Ratle, A.,

[729]

Ono, Isao,

[315]

P´ erez, Ra´ ul,

[650]

Rattray, L. Magnus,

[554]

Ono, N.,

[481]

Perez, Raul,

[662]

Rattray, Magnus,

[542, 582]

Ono, Norihiko,

[51, 176, 177]

P´ erez, Ra´ ul,

[728]

Ravichandran, B.,

[24]

[363]

Rahmani, Adel Torkaman,

[51, 176,

177]

20


Ravise, Caroline,

[223]

Sakai, S.,

[715]

Segre, Alberto Maria,

[890]

Rechenberg, Ingo,

[253]

Sakakibara, Yasubumi,

[231]

Seiden, P.,

[748]

Reed, J.,

[828]

Sakanashi, H.,

[831]

Sekaran, M.,

[80] [473]

Reilly, Kevin D.,

[381]

Sale, C.,

[245]

Seki, H.,

Reitman, J. S.,

[247]

Salzberg, Steven L.,

[455]

Sen, Sandip,

[448, 96, 777,

876]

Rekeczky, C.,

[605, 732]

Sanchis, A.,

[117]

Rendell, Larry A.,

[866, 867]

Sandholm, Tuomas,

[878]

Sannier, Adrian V., II,

[879, 255, 880]

Seredy´ nski, Franciszek, [100, 118]

Santos, M.,

[74]

Seredynski, F.,

Sanz-Bobi, M. A.,

[619]

Serra, R.,

Reynolds, Robert G.,

[375, 868,

869, 870, 871]

Rice, James P., Richards, Robert A.,

[839] [76, 93, 92,

178]

Sanz-Gonz´ alez, Jos´ e L., [211]

Ridella, Sandro,

[852]

Saravanan, N.,

[418, 427]

Riechmann, Thomas,

[337, 756]

Sarma, Jayshree,

[850]

Sase, M.,

[559]

Sato, Yoshiharu,

[544, 579, 590]

Riolo, Rick L.,

[16, 17, 181,

182, 183, 184, 288]

Riquelme, Jos´ e C., Rizki, Mateem M.,

[329] [373]

Rizki, Mateen M.,

[405, 458,

535, 872] [146]

Roberts, M.,

[215]

[46, 80, 493]

Sendhoff, Bernhard,

[309]

[77] [129, 130, 12,

131]

Sette, S.,

[25]

Shankaranarayanan, G., [694] Shapiro, D. I.,

[710]

Shapiro, Jonathan L.,

[542]

Shapiro, Jonathan,

[554, 582]

She, Linlin,

[694]

Sheppard, John W.,

[455]

Sheppard, Sheri D.,

[93, 92, 178]

[174]

Shi, C. Y.,

[563]

Schizas, Christos N.,

[547]

Shi, Chunyi,

[324, 660]

Schmeck, Hartmut,

[219]

Shi, Hong Bao,

[758]

Schmiedle, Frank,

[339]

Shibata, Takanori,

[799]

Shimohara, Katsunori,

[708]

Shimojima, Koji,

[456]

Shin, Seong-Hyo,

[709]

Shirao, Yoshiaki,

[392]

Sawargi, T.,

[754]

Schaeffer, Jonathan,

[193, 194, 195]

Schaffer, J. David,

Roberts, Gary,

Sen, S.,

[881, 256,

257, 882]

Schizas, C. N.,

Robertson, George G.,

[185, 288, 289]

Robinson, D.,

[628]

Rockett, Peter,

[707]

Rockett, P.,

[649]

Schneiner, B. J.,

[166]

Rodemann, Tobias,

[519]

Schnepf, Uwe,

[132, 135, 196]

Romaniuk, Steve G.,

[555]

Schnier, Thorsten,

[480]

Ros, Hans,

[817]

Schnier, T.,

[841]

Ros, Johannes P.,

[826, 827]

Sch¨ oder, Ingo,

[340]

Rosa, A.,

[548]

Schoenauer, Marc,

[419, 223]

Siegel, Eric,

[737]

Rosca, Justinian P.,

[667]

Schraudolph, Nicol N.,

[270]

Sierra, B.,

[665]

Ross, Peter,

[386]

Schrodt, P. A.,

[197]

Sikora, Riyaz,

[891]

Sim, Kwee Bo,

[734]

Sim, Kwee B.,

[614]

Simon, Herbert A.,

[474]

Simonini, P.,

[130]

Singer, Joshua A.,

[328]

Sirlantzis, K.,

[32]

Siu, Wan Chi,

[744]

Rost, Ursula,

[651]

Roventa, Eugene,

[400]

Rowland, J. J.,

[370]

Rowland, Jem J., Ruppin, E.,

[355] [349]

Russo, Marco,

[308, 332,

Shu, Lingyan,

Schultz, Alan C.,

[313, 376, 412, 503, 283, 812, 815, 883]

Schulze-Kremer, Steffen, [258] Schuurmans, Dale, Schwefel, Hans-Paul,

[193, 198] [884, 885,

886, 887, 888, 889]

Sebag, Mich´ ele,

[419]

Sebag, Michele,

[223]

Sebag, M.,

[229]

Secomandi, N.,

[513, 568]

Skolnick, Michael M.,

[199]

Segovia, Javier,

[652]

Slate, D. J.,

[278]

Segovia, J.,

[117]

Slavov, V.,

[226]

335, 556]

Rusu, Calin,

[73]

Saarenmaa, Liisa,

[877]

Saitta, Lorenza,

[387, 415]

Saitta, L., 717, 804, 805]

[840, 194,

195, 198]

Sklar, Elizabeth,

[666, 680,

705, 711, 724, 727]

[620, 227,

Authors

21

Smalz, R. W.,

[553]

Sun, Chuen-Tsai,

[382, 439]

Tsai, Y.-K.,

[609]

Smalz, Robert,

[407]

Sunol, A. K.,

[546]

Tsoukalas, Lefteri H.,

[639]

Smith, George D.,

[101]

Susu, Yao,

[565]

Tsuchiya, Kiichi,

[895] [897]

Smith, James,

[343]

Suzuki, Keiji,

[831]

Tu, James Zhen,

Smith, Matthew G.,

[356]

Sverdlik, William,

[375, 870, 871]

Tu, Yiqing,

[212]

Smith, R. E.,

[24]

Tabuchi, M.,

[561]

Tufts, Patrick,

[81]

Tai, Heng-Ming,

[628]

Tunstel, E.,

[586]

Takahama, T.,

[715]

Twardowski, Kirk E.,

[202, 203]

Takahashi, H.,

[626]

Twardowski, Kirk,

[408]

Takahashi, Shingo,

[312]

[875]

Takai, Yoshiaki,

[544, 579, 590]

[39]

Takano, T.,

[528]

So, Yee Leung And Leo, [899, 900]

Tamaki, H.,

[588, 692]

Solaiman, Basel,

[331]

Tamburino, Louis A.,

Son, So-Hyun,

[341]

Smith, Robert Elliot,

[150, 153,

179, 180]

Smith, Robert E.,

[311, 85]

Smith, Stephen F.,

[809, 280,

836, 873, 874]

Smith, Stephen J., Snowdon, Jane L.,

Song, Jong-Cheol,

[341]

Spackman, Kent A.,

[252]

Spears, William M.,

[422, 494, 786, 787, 236, 237, 275, 892]

Spiessens, Piet,

[200]

Spirov, A. V.,

[678]

Spohn, Bryan G.,

[104]

Stafford, Brian,

[664]

Stafylopatis, A.,

[538, 114]

Stanley, Donald A.,

[166]

Starita, Antonina,

[444]

Stefano, Claudio De, Steiner, S. J., Stender, Joachim, Stolzmann, Wolfgang,

[610] [630] [893] [23, 28, 35]

[373, 405,

458, 535, 872]

Uchikawa, Yoshiki,

[52, 414, 69,

466, 469, 205]

Uchikawa, Y.,

[425, 486, 91,

673]

Uchiyama, T.,

[626]

Ueda, Kanji,

[346]

Ugur, A.,

[434, 602]

Tani, N.,

[754]

Ulyanov, S.V.,

[75]

Tanomaru, J.,

[663]

Unemi, T.,

[421]

Tarroux, Philippe,

[37]

Urbanˇ ciˇ c, Tanja,

[738]

Taura, Toshiharu,

[103]

Urbieta Parrazales, R.,

[622]

Taura, T.,

[561]

Urzelai, Joseba,

[297]

Tazaki, Eiichiro,

[661]

Ushida, A.,

[605, 732]

Tazaki, E.,

[528]

Vafaie, Haleh,

[217, 471,

531, 606, 788]

Teller, Astro,

[294]

Terano, Takao,

[53, 482]

Terano, T.,

[562, 585]

Terao, Makoto,

[302]

Vandersmissen, B.,

Teunis, M.,

[642]

van den Broek, Wilhelmus H. A. M.,

Thagard, Paul R.,

[823, 160]

Theodosius, A.,

[174]

Thompson, A. C.,

[463]

Thomson, Peter,

[722]

Tian, Fengzhan,

[324]

Venturini, G.,

[437]

[512]

Verdegay, Jose Luis,

[500, 501]

Villar, Pedro,

[338]

Valastro, G.,

[129]

Valenzuela-Rend´ on, Manuel,

[179,

206, 207] [789]

[671]

Velasco, Juan R.,

[450]

Veloso, Manuela,

[294]

Venturini, Gilles,

[428, 430,

459, 95, 898, 259, 260]

Stone, P.,

[264]

Stonier, R. J.,

[755]

Stonier, Russel James,

[457]

Stork, David G.,

[894]

Vitri` a, Jordi,

[293]

Stroud, Phillip D.,

[645]

Tokinaga, Shozo,

[201]

Vivarelli, Francesco,

[657]

Su, Suchen,

[895]

Tong, Wang,

[658]

Vlachavas, I.,

[639]

Succi, S.,

[748]

Toombs, R.,

[828]

Voicu, Liviu I,

[747]

Sugimoto, Okamoto J., [896]

Toro, Miguel,

[329]

Wada, Mitsuo,

[654]

Sugimoto, Y.,

Torresen, Jim,

[305]

Wada, M.,

[700]

Toto, E.,

[513, 568]

Wah, B. W.,

[484]

Tochinai, K., Todd, Peter M.,

[402]

Sullivan, Charles. C. W.,

[560]

[800, 855,

856, 857]

Sullivan, J. W. C.,

[653]

Toussaint, Marc,

[333]

Wah, Benjamin W.,

[507]

Summers, R.,

[463]

Trianni, Vito,

[29]

Wahab, A. A.,

[850]

22


Walters, M.,

[633]

Wolff, K.,

Wang, Chi-Hsu,

[319]

Wong, Man Leung,

[352] [416, 426,

Young, D. K.,

[232]

Ypsilantis, J.,

[411]

Yurramendi, Yosu,

[574]

Zang, Yuwen,

[473]

Zannoni, Elena,

[870]

Zelinka, Ivan,

[759]

Zhang, Byoung-Tak,

[438, 771]

Zhang, Lei,

[473]

Zhang, Liming,

[467]

498, 577]

Wang, G., Wang, Houfeng, Wang, J. W., Wang, Lisong,

[718]

Wu, Annie S.,

[354]

Wu, Ming-Da,

[382, 439]

Wu, Wen,

[409]

Wu, Yan,

[758]

Wu, Y.,

[454, 763]

Xiang-Wu, Meng,

[716]

Xie, Weixin,

[461, 487]

Xiong, N.,

[703]

Zhang, Yan-Ning,

[213]

Xu, Lei,

[374]

Zhang, Z. H.,

[563]

Yam, Kim Fai,

[899, 900]

Zhao, Long,

[540]

Yamafuji, K.,

[75]

Zhao, Q. F.,

[550]

Yamagata, Y.,

[559]

Zhao, Qiangfu,

[526] [697] [473]

Wang, Lori A.,

[168, 170]

Wang, Y.,

[669, 702]

Warwick, Kevin,

[99, 165]

Wasson, III, E. C.,

[719]

Watson, Richard A.,

[318]

Wazlawick, Raul Sidnei, [462] Wechsler, Harry,

[471, 531, 606]

Wei, Chengjian,

[760]

Weicker, K.,

[764]

Weicker, N.,

[764]

Weihua, Li.,

Yamamura, M.,

[504]

Yanda, Li,

[537]

Yang, Jihoon,

[634]

Yang, Jun-an,

[214]

Yang, Luxi,

[760]

Yang, X.,

[486]

Yano, K.,

[421]

Yao, Leechter,

[536]

[539]

Weiß, Gerhard,

[63, 147, 801]

Weller, P. R.,

[463]

Westerdale, Thomas H., [18, 19] Westerdale, Thonmas H.,

[107]

Whigham, P. A.,

[410, 485]

While, Lyndon,

[306, 746]

Whinston, Andrew B.,

[201]

Whiteson, S.,

[264]

Yap, Kim-Hui,

[344]

Whitley, Darrell,

[429, 291]

Yasunaga, Moritoshi,

[310]

Yates, Derek F.,

[45, 55, 13]

Yee, H.,

[411]

Yao, Xin,

Wilson, Stewart W.,

[35, 54, 777, 785, 875, 186, 187, 254, 290, 188, 189, 190, 191, 192]

[479, 551,

592, 596, 598, 647]

[317, 564,

591, 659, 670]

Winston, Flaura K.,

[22]

Yoshihara, Ikuo,

[310]

Wnek, J.,

[850]

Yoshinaga, Kazuyuki,

[482]

Zhao, Q.,

[580]

Zhao, Rong-Chun,

[213]

Zheng, Nan-Ning,

[483]

Zhenmei, Gu,

[611]

Zhenya, He,

[565]

Zhou, Hayong (Harry), [156, 157] Zhou, Y. H.,

[563]

Zhou, Yuanhui,

[660]

Zhuang, Zhenquan,

[214]

Zini, Floriano,

[387, 415]

Zizka, J.,

[532]

Zmuda, Michael A.,

[373, 405, 458]

Zyl, Jacobus van,

[371]

total 893 articles by 1168 different authors

Subject index

4.7

23

Subject index

All subject keywords of the papers given by the editor of this bibliography are shown next.

acupuncture, agents,

[473]

[83, 101, 115, 320, 30, 302, 315, 36]

autonomous,

[346]

chemical processes,

associative memory,

[836]

chemistry

automata,

[876]

[770, 795,

832, 300]

coordination,

[832, 29]

evolving,

[742]

fuzzy,

lineless,

[741]

learning,

[813, 791,

finite state,

[362]

learning,

[506, 579, 590]

autonomous agents, autonomous agents,

801, 304] [691, 704,

721, 331] [792]

agriculture vehicles, AI,

[741]

ALECSYS,

[791, 134,

792, 265, 266] [277, 554,

571, 102] [676, 681]

[98]

strategy,

[269]

BEAGLE,

[241]

[208]

special,

[208]

Populus

clone

discrimination,

[830]

[187, 290]

book review Holland 1992,

[110]

Boolean functions,

[186, 105, 109]

ant trail,

[678]

Boolean multiplexer problem,

applications [830]

industrial,

[432]

[632] [243, 522,

[14, 18, 16,

17, 15, 19, 12, 238, 13]

CAD, optimization,

architecture

[168,

869, 870]

bucket brigade,

forestry,

[850, 849, 46, 405, 441, 78, 472, 96, 589, 650, 119, 356, 215]

cost-sensitive,

[634]

plastic,

[671]

protein fold,

[258]

shape design,

[259] [86, 112] [11]

[158, 145, 156, 139, 159, 186, 187, 160, 161, 290, 271, 232, 149, 155, 157, 181, 184, 185, 288, 189, 123, 129, 130, 140, 141, 162, 163, 166, 169, 172, 179, 193, 194, 197, 207, 124, 12, 272, 164, 168, 182, 289, 200, 202, 121, 125, 126, 132, 133, 136, 143, 144, 278, 150, 170, 171, 173, 174, 180, 183, 195, 196, 206, 131, 128, 134, 147, 151, 152, 153, 176, 178, 192, 199, 201, 122, 127, 135, 13, 137, 138, 146, 154, 165, 167, 175, 203, 204, 37, 38, 39, 42, 43, 45, 46, 47, 50, 51, 53, 54, 57, 60, 61, 62, 63, 64, 67, 68, 71, 72, 74, 76, 77, 78, 80, 81, 82, 83, 85, 87, 88, 89, 90, 92, 93, 95, 96, 98, 99, 100, 101, 102, 103, 104, 108, 114, 115, 116, 117, 118, 22, 31, 33, 35, 20, 21, 23, 28, 36]

classifier systems

[825]

agents,

[29]

[178]

APL,

[70]

applications,

[106]

bibliography,

[208]

[178, 76, 93]

cancer

character recognition,

230, 742]

breast,

[719, 363]

[880, 43, 413,

coevolution,

[32]

[24]

diagnosis,

[363]

[705]

melanoma,

[903]

convergence,

[49]

[398]

cellular automata,

[602, 230]

cooperating,

[56]

CFS-C,

[181]

credit allocation,

[188, 190]

481, 720]

assembly

[414]

classifier systems,

[43, 569, 705]

ARTMAP,

local improvement,

bucket brigade,

classifier systems,

animats,

animats,

[108]

classifier system,

[785]

artificial life,

[671]

chromatography,

[574, 627]

BOOLE,

artificial intelligence,

chemometrics,

classifier implementation

learning,

[116]

aesthetics,

[102]

classification

Bayesian networks

[33]

animation computer graphics,

structural,

[134, 265,

423, 266]

biometrics

analysing LCS

Animat problem,

AutonoMouse,

bibliography

in machine learning, [574, 347]

Markov model,

[546, 695]

AGIL,

analysing GA,

recombination,

[108, 322]

process,

classification, [898, 259]

backgammon

[253, 399]

crossover,

autonomous robot,

analytical,

chromosomes

[770, 795,

832, 446, 80, 82]

machine learning, training,

[423]

[891]

comparison to Q-learning,

[58]

24


distributed,

[177, 266]

in machine learning, [441]

design,

emergency,

[120]

machine learning,

fuzzy,

emergent behaviour, [276, 142]

multi-objective

[850, 732, 342]

[738]

[475, 84, 511, 532, 545, 586, 687, 688, 736, 755, 296]

optimization,

[339]

evolutionary stability,

robot,

[55]

neural network learning, fuzzy,

[205, 41, 52, 69, 73, 75, 79, 84, 91, 97, 111, 119, 25]

games,

[44, 30]

neural networks,

[113]

neural networks,

[318]

[244]

cooperation,

[127]

credit assignment,

[810]

[114]

neural networks in protein secondary structure prediction,

critics

[657]

classifier systems,

[107]

simulated annealing, [46, 605, 102, niching,

[59, 66]

parallel,

[40]

pre-prosessing,

[27]

real vectors,

[191]

competition,

[731]

culture,

[775]

review,

[65]

complex systems,

[47, 87]

data mining,

[761, 324]

rules,

[148]

complexity,

[768]

causal networks,

XCS,

[94, 105, 109]

[48]

telecommunications, [209]

comparisons classifier,

classifiers,

[821, 254, 15, 19, 279, 787, 238, 234, 791, 259, 137, 431, 81, 102, 327, 304]

[69]

neural network,

[709] [901, 501]

[420]

computer science [384]

Turing, consumer choices,

[648]

context free languages,

[270]

control,

[488, 495]

GP,

[224]

learning,

[419]

[212]

data structures,

[840]

databases,

[426]

decision rules,

[390]

forestry,

[877]

[663]

knowledge,

[660]

[809]

relations,

[416]

rules,

[830]

datamining,

[335]

deception,

[831]

decision making,

[472]

decision support,

[30]

DELVAUX,

[568]

context free grammars, [509]

cobweb model

coding,

computer graphics,

[58]

computing

fuzzy,

learning,

classifier systems,

operating systems,

classifiers

clustering,

crossover,

732]

[876] [246, 891,

138, 549, 631, 757]

Gray,

[256, 257]

autonomous vehicle, [741] set based,

[557]

classifier systems, coding?,

[266]

[480]

fuzzy, coevolution,

[137, 492, 580, 647, 666, 680, 228, 705, 269, 711, 717, 720, 724, 734, 753, 118, 764, 33, 363, 215]

coevolution

[388, 399, 450, 445, 469, 500, 575, 607, 630, 633, 644, 715]

laser,

[645]

magnetic learning,

[314]

classifier systems,

[24]

mobile robot,

[292]

learning,

[550, 620]

mobile robots,

[38]

machine learning,

[522, 230]

neural network,

[429]

[39]

nonlinear,

[765]

PID,

[73]

diagnosis cancer,

[739]

fault,

[165, 546]

faults,

[611]

medical,

[674]

disjunctive concepts,

[387]

distributed systems comparison,

load balancing, decision tree classifiers, equilibrium GA,

[287]

[379]

robot,

[544]

divide et conquer,

[870]

DNA,

[675]

[496, 499,

586, 633]

GA better than neural approach, [114]

rule based,

[114]

proteins,

[664]

traffic lights,

[399]

sequence assembly,

[267]

GA vs GP,

[339]

greedy learning,

[761]

controller

in causal networks,

[212]

fuzzy,

in classification,

[431]

controllers

[685]

dynamic control,

[806]

E-MORPH,

[405]

economic modeling,

[90]

Subject index

economics,

25

[769, 756, 358]

fault diagnosis,

[99]

cobweb model,

[648]

feature selection,

[650, 671, 322]

consumption,

[482]

fitness

game theory,

[863, 864, 436]

evolutionary,

[337]

Nash equilibrium,

[118]

games,

[705, 312]

credit,

[238]

learning,

[729]

finance,

[743]

noisy evaluation,

[582]

backgammon,

[269]

royal road,

[696]

checkers,

[730]

fuzzy,

[382] [727]

financial time series, [365]

fitness function

investment advising, [60] models,

[587, 595, 740]

portfolio management, editorial,

[201]

[284]

human,

[705]

hockey,

progressive,

[362]

iterated prisoner’s dilemma,

food science

electrical power systems, [155]

food identification,

[364]

embedded systems,

meat quality,

[323]

quality,

[360]

forecasting,

[743]

[384]

emergent phenomenon rules, engineering, aerospace,

[453] [155] [113, 24, 311,

352]

chemical,

[891]

chemistry,

[546]

forest fires simulated, forestry,

[877]

formal languages,

[509]

[92]

FuGeNeSys,

mining,

[834]

functions

[165, 411,

process,

[695]

software,

[329]

structural,

[178, 31]

ergonomi [111] [847]

evolution,

[828, 242, 253, 775, 887, 47, 87, 720, 337]

grammars,

Royal road,

pursuit-evasion,

[342]

Tron,

[680]

Garel,

[404]

GATES,

[671]

generations [634] [671]

[231] [249]

[498]

rules,

genetic programming

control,

[445, 469]

learning,

[584]

fuzzy reasoning,

[79]

fuzzy relational equations,

[404]

fuzzy rules,

[380, 425, 622] [388, 71]

[228]

fuzzy systems,

learning,

[855, 857]

[206, 122, 205, 41, 398, 457, 473, 511, 518, 546, 695, 758, 300, 303, 363, 371]

fuzzy systems

[352]

decision trees,

[226]

games,

[737]

learning,

[433, 636, 689]

machine learning,

[534, 339, 356]

over fitting,

[358]

system identification,

[216]

GLEAM,

[776]

gradient descent,

[758]

classifier,

[52]

hybrid,

[335]

learning,

[381, 308]

rule based,

[111]

graph partitioning, graphs

[305, 310] [155, 899,

900, 400, 60, 674, 723]

control,

grammar

519, 560]

evolvable hardware

[241, 838, 770, 839, 386, 410, 54, 446, 81, 224, 496, 498, 499, 508, 510, 573, 577, 629, 652, 667, 677, 706, 708, 725, 294, 330, 345, 355, 215, 369, 302]

[589] [804, 452]

host-parasite,

[903]

genetic programming, [646]

fuzzy logic,

fuzzy sets,

[243, 388,

[308]

fuzzy

[229]

expert systems,

[746, 306]

classification,

continuous,

application,

poker,

[30]

genetic algorithm

504, 99]

evolution strategies,

[737, 328]

5000,

mechanical,

ethology,

Othello,

GENESIS,

[148]

information,

[439, 597]

30,

context-free grammars,

communal,

power,

[38]

learning,

nautral language,

[340]

grammars learning,

learning,

[611]

G protein,

[675]

medical,

[473]

GABIL,

[494]

layout,

rule learning,

[619]

GALE,

[794]

Gray code,

[655] [506]

[420] [256, 257]

26


Hamming weights,

[272]

initial population,

hardware,

[836]

insurance underwriting, [401]

evolvable, hierarchies,

[600, 722]

intelligence,

[242]

Internet,

[666]

intervals,

[899, 900]

introns,

[302]

[195]

human learning modeling, hybrid,

[183]

[758]

competitive learning,

[379]

Kohonen VQ,

[379]

machine learning,

[738, 367]

neural networks,

[547, 639,

crossover,

[224]

iterated prisoner’s dilemma,

[101]

job shop problem,

[442]

quantum computing, [213, 214]

JPN,

[357]

softcomputing,

[335]

justice

SOM,

[359]

679, 680, 365]

hyperschema theorem,

machine learning,

[249]

French,

[430]

[869]

knowledge acquisition,

image processing,

[802, 803, 172, 199, 841, 486, 651, 293]

[562, 585]

knowledge based systems,

[761, 316,

345]

boundary detection, [220] deconvolution,

[344]

knowledge discovery,

edge detection,

[213]

knowledge-based systems,

feature extraction,

[215]

Lamarckian learning,

[813]

LamBaDa,

[752]

LAN,

[880]

language,

[794]

pattern classification,

[471]

[634] [53]

pattern recognition, [305, 310] quantization,

[72]

segmentation,

[37, 378]

languages

imaging IR,

[747]

medical,

[651]

multi-spectral,

[471]

grammars,

machine translation, [512] learning, 247, 248, 233, 790, 259,

immune networks learning,

[552]

immune system,

[797, 748]

implementation,

[181]

APL,

[70, 90]

Cray Y-MP8/864,

[825, 391]

FPGA, transputers,

[790] [239]

induction,

[371] [868, 869,

234, 235, 870, 871]

inheritance Lamarckian,

[828, 833, 866, 873, 794, 802, 821, 822, 807, 810, 774, 817, 886, 775, 893, 837, 849, 887, 772, 773, 245, 846, 862, 877, 891, 898, 263, 768, 818, 831, 204, 394]

evolution,

[885]

multiplexer,

[838]

optimisation,

[888, 889]

Q-,

[481]

[309]

learning classifier systems fitness,

machine learning agents,

[684, 691,

704, 721, 29]

associative,

[408]

autonomous vehicle, [741]

learning

[310]

incremental GA,

inductive learning,

[541]

[874, 882, 796, 803, 806, 867, 881, 797, 241, 823, 842, 809, 834, 843, 879, 844, 255, 880, 239, 808, 820, 848, 863, 875, 769, 777, 784, 786, 789, 240, 811, 812, 845, 852, 853, 858, 872, 776, 780, 787, 132, 798, 800, 246, 813, 829, 836, 174, 851, 859, 860, 883, 892, 767, 771, 778, 788, 236, 791, 134, 793, 814, 815, 825, 826, 827, 840, 847, 864, 876, 201, 897, 899, 900, 375, 770, 779, 781, 783, 785, 275, 135, 792, 795, 801, 805, 280, 286, 832, 839, 854, 856, 861, 895, 260, 376, 39, 378, 379, 380, 383, 385, 265, 45, 387, 388, 389, 48, 390, 395, 396, 50, 398, 399, 400, 401, 404, 405, 410, 412, 413, 58, 414, 420, 217, 422, 426, 427, 64, 430, 431, 432, 450, 436, 219, 439, 440, 442, 443, 444, 221, 446, 73, 447, 448, 449, 222, 451, 452, 453, 455, 457, 458, 459, 467, 468, 470, 472, 473, 474, 475, 476, 477, 479, 482, 483, 486, 488, 491, 493, 494, 495, 496, 497, 499, 500, 503, 505, 507, 508, 510, 511, 512, 515, 517, 518, 519, 520, 525, 527, 529, 530, 531, 532, 533, 535, 536, 540, 542, 544, 545, 546, 547, 548, 550, 92, 553, 554, 560, 561, 562, 563, 565, 567, 569, 571, 225, 573, 577, 578, 580, 581, 582, 583, 96, 585, 587, 588, 591, 595, 600, 601, 602, 604, 607, 609, 610, 612, 613, 617, 620, 622, 626, 631, 632, 633, 634, 636, 637, 639, 641, 642, 643, 644, 645, 648, 649, 651, 657, 658, 659, 661, 662, 664, 666, 668, 669, 670, 671, 673, 674, 675, 676, 678, 679, 680, 681, 682, 687, 688, 692, 694, 695, 698, 701, 703, 705, 269, 706, 709, 711, 114, 712, 714, 715, 717, 718, 719, 720, 722, 723, 724, 726, 727, 728, 730, 731, 734, 736, 739, 740, 742, 743, 745, 748, 749, 751, 752, 753, 754, 755, 756, 757, 758, 231, 761, 763, 764, 294, 295, 296, 299, 300, 303, 305, 307, 310, 312, 313, 317, 318, 321, 322, 324, 327, 328, 329, 31, 335, 337, 338, 212, 344, 345, 350, 352, 355, 358, 363, 365, 903, 371, 298, 301, 302, 304, 315, 346]

[26]

linquistics,

[340]

load balancing,

[255, 880]

Bayesian,

[513]

certainty factors,

[397]

classification,

[471, 589]

coding,

[351]

concept,

[465]

concept descriptions, [227] concepts, control,

[428] [765, 445,

469, 575]

cooperative,

[782]

Subject index

27

cost,

[514]

pattern recognition, [373, 293]

diagnosis,

[670, 701, 723]

decision,

[490]

Q,

dianostics,

[473]

decision making,

[835]

reinforced,

electromyography,

[174]

EMG,

[547]

epidemiology,

[89, 22]

human genetics,

[441]

[54, 421] [418, 504,

538, 268, 690, 311]

decision trees,

[638, 210]

reinforcement, detection,

[747]

diagnosis,

[411]

[614, 685, 725, 330, 349, 353, 368, 369, 264, 372]

reinforcement learning,

[357]

disjunctive concepts, [415]

relations,

[416, 485]

learning,

[625]

features,

representations,

[480]

neurology,

[547]

review,

[890, 816]

pre-eclampsia,

[366]

robotics,

[325, 334, 348]

urology,

[701, 723]

robots,

[218]

rule based,

[568, 619, 630]

rule induction,

[830]

rule-based,

[25]

rules,

[677]

scheduling,

[250, 484, 570]

structural,

[598]

supervised,

[393, 689]

syntactic rules,

[541]

[702, 356]

first order logic rules,

[437]

fuzzy,

[819, 425, 456, 461, 466, 487, 501, 556, 576, 584, 586, 621, 650, 710, 308, 314, 320, 332]

fuzzy rules,

[460]

fuzzy sets,

[804]

gait,

[618]

game,

[628]

game playing,

[683]

games,

[382, 746, 30,

306]

gene linkage,

[343]

genetic programming,

[433, 323,

360, 364, 366, 370]

systems,

[593]

temporal,

[489]

training,

[386]

grammar,

[509]

Turing,

[663]

grammars,

[640, 655, 340]

visual concepts,

[606]

hierarchical,

[667]

machine learning?,

[713]

hybrid,

[824]

machine learningi,

[901]

in optimization,

[646]

macromolecules

incremental,

[616]

proteins,

induction,

[498, 672]

man-machine interface, [111]

inductive,

[539, 699]

manufacturing

[657]

control,

[567]

inference,

[157, 362]

scheduling,

[588]

landscapes,

[750]

simulation,

[39]

medicine,

[121]

steel mill,

[106]

modeling,

[464]

textile,

[25]

motion,

[653]

multi-agent, natural

language

[629]

processing,

manufacturing control,

[778]

mazes,

[895]

meat quality,

[370]

[526]

medical imaging neural network,

[555]

melanoma, neural networks,

[545]

Midgard,

[255]

military fighter aircraft,

[24]

[903]

[166]

mobile robot,

[521]

mobile robots,

[67]

autonomous,

[614]

obstacle avoidance,

[425]

mutation rate 0.001,

[634]

navigation robot,

[292]

neural network fuzzy,

[565]

PCA,

[709]

neural networks,

[129, 172, 249, 872, 893, 261, 262, 780, 800, 851, 859, 860, 771, 825, 781, 165, 62, 219, 462, 601, 642, 730, 294, 31, 350, 363]

neural networks backpropagation,

[402, 572,

677, 693]

Bayes,

[324]

cascade correlation, [657] classification,

[563, 634, 709]

control,

[291, 545,

607, 735]

evolution,

[490]

fuzzy,

[537, 319]

games,

[328]

[244, 429,

medicine

211, 336]

Othello,

messy GA,

phosphate,

[502]

[424]

[665]

mining

geometry,

industrial applications,

melanoma,

[737]

cancer,

genetic programming, [665, 719, 903]

Hopfield,

[294]

[716]

28


hybrid,

pattern recocognition,

[335]

Kohonen,

[461, 487,

pattern recognition,

[796, 901, 244, 872, 825, 434, 220, 531, 535, 606, 679, 734, 739, 293, 334, 363]

745, 359]

learing,

REGAL,

[172]

[637]

896, 402, 438, 524, 557, 594, 624, 696, 744, 333,

[901, 374, 799, 377, 384, 391, 403, 406, 407, 409, 434, 454, 463, 492, 516, 521, 528, 537, 543, 550, 551, 558, 559, 564, 566, 572, 596, 599, 603, 605, 608, 647, 652, 654, 660, 665, 697, 700, 708, 716, 733, 759, 297, 309, 316, 319, 336, 354, 292, 361, 298]

learning rate,

[693]

learning rules,

[865]

machine learning,

[670, 762]

multistrategy learning,

894, 392, 435, 523, 553, 592, 623, 686, 735, 326,

[902]

perceptron,

[555]

perceptrons,

[516]

radial basis function, [594, 599] RBF,

[763]

signal processing,

[214]

SOM,

[745]

structure,

[523, 558,

566, 734]

training,

[381, 487]

neural networks 7learning, NEWBOOLE,

[635]

[96]

non-supervised learning, [462]

load balancing,

[544]

control, optimization,

[223] [853, 793, 897]

combinatorial,

[396]

learning,

[884]

multi-objective,

[21]

Othello,

[737]

[278]

classification,

[563]

[879, 880, 249, 851, 790, 176, 792, 37, 387, 391, 219, 74, 477, 614]

face,

[471]

machine learning,

[707]

spectrum,

[322]

pattern regocnition,

[458]

[661]

pharmacology,

[675]

planning,

[812]

politics,

popular,

[374, 878, 656] [323]

manipulator motion, [653] robotics,

[791, 376,

412, 503, 668, 348]

control,

[73, 266, 586]

learning,

[417, 423,

457, 299, 369] [322]

mobile,

[132, 783, 135, 38, 425, 75, 457, 496, 499, 560, 573, 614, 618, 629, 633, 325, 352, 292, 361, 368, 372]

[47, 87]

[185, 289]

100,

[671]

50,

[634]

small,

[560]

prediction,

machine learning,

[197]

[478]

protein folding,

[273]

[297, 368, 372]

learning,

project estimation,

learning with GA,

autonomous,

[110]

n players,

[575]

[468]

classifier systems,

population size,

intelligent control,

microbial spoilage in meat,

pollution fuel spills,

[237]

robot

fuzzy,

military,

[198, 35]

evolutionary computation,

Petri nets

multi-,

[318]

navigation,

[167]

path planning,

[533]

space exploration,

[311]

wall-climbing,

[83]

robots [659]

[329]

autonomous,

[520]

machine learning,

[218]

mobile,

[67]

routing,

[893] [258, 657]

proteins

[778]

distributed,

[97]

rule based system,

[674]

rule based systems,

G,

[675]

ligands,

[505]

[874, 184, 787, 175, 217, 444, 472, 473, 500, 82, 511, 518, 532, 11, 624, 701, 723, 743]

rule based systems

psychology

fuzzy,

decision making models, [255]

quantum computer, parallelism,

[471] [285, 808,

classifier systems,

secondary structure, [657]

parallel GA,

parallel GA?,

character,

production economics

operators

Landsat MSS, 274, 251]

prisoner’s dilemma

operating systems

remote sensing

review,

pattern recognition learning,

[415]

[819, 466, 467, 78, 82, 501, 576, 111]

[835]

rule generation,

[112]

rule-based systems,

[301]

[295]

[444]

reasoning parameters,

[270]

fuzzy, case-based,

PASS,

[338]

[739]

rule-based sytems,

[398]

[568]

recycling patent,

rules,

[802, 822, 162, 749, 757]

waste plastic,

[671]

[179, 283, 252, 246, 899, 900]

Subject index

learning,

29

SOM

[841]

SAMUEL,

[246, 813]

SANE,

[490]

spectroscopy,

scheduling,

[250, 249, 862, 470, 484, 529, 100, 692]

classification,

[359]

[39]

FMS,

[443]

heterogeneous machine, heterogeneous machines,

[570]

JSS,

[844, 609, 682]

power plant,

[504]

processors,

[384]

sequence of classifiers,

[16]

shape design,

[93, 31] [92] [103]

signal processing,

[549, 631]

blind source separation,

[214]

TSP

[323, 360,

mass,

[366]

NIR,

[671]

wavelength selection,

[360, 364,

tutorial machine learning, machine

learning

[549, 615]

paradigms,

[766]

[742]

UK*,

[20]

StGA,

[579, 590]

urology,

[674]

subdivision,

[367]

VCS,

[194]

sunspots,

[587]

vehicles

system identification,

[798, 363]

telecommunications, routing,

[216]

[631, 684, 209] [97]

vector quantization, [447]

texture,

[234]

wavelets,

theory

[697]

[760]

statistical inference,

[367]

[634]

[137]

machine learning,

903]

text book,

feature selection,

[670]

[366]

genetic programming,

shape generation,

Mackey-Glass, tracking,

364, 370]

[690]

[365]

[355]

infrared,

dynamic,

3D,

learning,

forecasting,

automotives,

[307]

autonomous,

[815, 114]

navigation,

[114]

version spaces,

[868, 869,

870, 871]

vision,

[234]

VLSI design,

[339, 362]

signatures,

[486]

machine learning,

[604]

simulated annealing,

[694]

Turing,

[663]

wave length,

[671]

TIERRA,

[47, 87]

wavelets,

[697]

[768]

XCS,

[20]

ZCS,

[54]

simulation hockkey,

[727]

Tierra,

soccer game,

[395]

time series

30

4.8


Annual index

The following table gives references to the contributions by the year of publishing.

1967,

[828]

1980,

[874]

1981,

[158, 833]

1983,

[247, 866, 873]

1984,

[148, 882]

1985,

1995,

[434, 435, 436, 437, 219, 67, 438, 439, 220, 440, 68, 441, 442, 69, 443, 70, 444, 71, 221, 72, 445, 446, 73, 447, 448, 449, 222, 451, 74, 75, 452, 453, 76, 454, 223, 77, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 266, 465, 466, 467, 468, 469, 470, 78, 471, 472, 473, 474, 475, 476, 79, 477, 478, 267, 479, 80, 480, 481, 482, 81, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 224, 495, 496, 497, 498, 499, 500, 82, 501, 208]

1996,

[502, 503, 504, 505, 506, 508, 509, 83, 84, 510, 511, 85, 512, 513, 86, 514, 515, 87, 517, 518, 519, 520, 521, 522, 523, 524, 525, 88, 526, 528, 529, 89, 530, 531, 532, 533, 534, 535, 536, 537, 539, 540, 541, 542, 543, 90, 268, 544, 545, 91, 546, 547, 549, 550, 551, 552, 92, 553, 554, 93, 555, 556, 557, 558, 560, 561, 562, 94, 95, 563, 564, 565, 566, 567, 568, 569, 571, 572, 225, 11, 573, 574, 575, 576, 577, 578, 579, 581, 582, 583, 96, 584, 585, 586, 587, 588, 589, 590, 592, 593, 594, 595, 596, 97, 597, 598, 599, 600]

507, 516, 527, 538, 548, 559, 570, 580, 591,

608, 618, 630, 640, 650, 107, 109,

[98, 601, 602, 603, 604, 605, 606, 609, 610, 611, 612, 99, 613, 614, 615, 616, 617, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 631, 632, 633, 634, 101, 635, 636, 637, 102, 638, 641, 642, 643, 103, 226, 644, 645, 646, 647, 648, 651, 652, 104, 653, 654, 655, 656, 105, 657, 106, 659, 660, 661, 662, 663, 108, 664, 665, 666, 667, 669, 670, 671, 672, 673, 674]

607, 100, 629, 639, 649, 658, 668,

682, 230, 111, 710, 721,

[675, 676, 677, 678, 679, 680, 681, 228, 683, 684, 685, 686, 687, 229, 688, 689, 690, 692, 693, 694, 695, 696, 697, 110, 698, 699, 700, 702, 703, 112, 704, 113, 705, 269, 706, 707, 708, 711, 114, 712, 713, 714, 715, 716, 717, 718, 719, 722, 723, 724, 725, 726]

227, 691, 701, 709, 720,

[794, 796, 145, 802, 803, 806, 156, 14, 821, 822, 867, 881, 18]

1986,

[797, 139, 241, 248, 159, 823, 842, 186, 187]

1987,

[807, 809, 160, 161, 834, 843, 16, 17, 254, 290, 188, 879, 198]

1988,

[271, 232, 273, 276, 149, 155, 810, 281, 282, 157, 285, 844, 181, 184, 185, 288, 189, 255, 880, 256, 884, 885]

1989,

[774, 123, 129, 130, 239, 140, 141, 242, 243, 808, 817, 820, 162, 163, 15, 166, 250, 169, 848, 172, 863, 179, 253, 875, 190, 193, 194, 257, 197, 886, 207, 19, 901]

1990,

[769, 775, 777, 124, 12, 272, 784, 233, 274, 786, 789, 240, 142, 244, 279, 283, 811, 812, 164, 249, 168, 845, 252, 850, 852, 853, 855, 858, 287, 868, 182, 872, 289, 200, 893, 894, 202, 261, 262]

1991,

[373, 776, 121, 125, 126, 780, 787, 238, 132, 133, 136, 798, 143, 144, 278, 800, 150, 246, 813, 829, 830, 836, 837, 838, 170, 171, 849, 173, 174, 851, 859, 860, 180, 869, 183, 195, 196, 883, 887, 892, 896, 206]

1992, 782, 245, 847, 877, 263]

1993, 781, 799, 286, 865,

1994,

128, 151, 251, 888,

131, 152, 176, 889,

234, 153, 857, 890,

[374, 766, 767, 771, 772, 773, 270, 235, 788, 236, 790, 791, 134, 793, 814, 815, 825, 826, 827, 835, 840, 862, 178, 864, 870, 871, 876, 191, 891, 199, 201, 897, 898, 259, 899,

[375, 765, 768, 770, 120, 122, 779, 783, 785, 237, 275, 135, 792, 795, 13, 137, 138, 146, 801, 804, 805, 154, 280, 284, 816, 818, 819, 165, 831, 832, 167, 839, 841, 175, 177, 854, 856, 878, 258, 895, 203, 204, 205, 260, 291, 902]

778, 147, 846, 192, 900,

127, 277, 824, 861,

[376, 377, 37, 38, 39, 378, 379, 380, 381, 40, 382, 41, 383, 42, 384, 385, 43, 265, 44, 216, 45, 46, 386, 47, 387, 388, 389, 48, 49, 390, 391, 392, 393, 394, 395, 396, 397, 50, 398, 51, 399, 52, 400, 401, 402, 403, 404, 405, 406, 407, 53, 408, 409, 410, 54, 55, 411, 412, 413, 56, 57, 58, 414, 415, 59, 416, 60, 417, 418, 419, 420, 421, 217, 61, 422, 423, 424, 425, 218, 426, 62, 427, 428, 63, 429, 64, 65, 66, 430, 431, 432, 433, 450]

1997,

1998,

1999,

[727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 115, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 116, 117, 754, 118, 755, 756, 757, 758, 231, 759, 760, 761, 119, 762, 763, 764]

2000,

[20, 293, 294, 295, 296, 297, 298, 21, 209, 299, 300, 301, 302, 303, 304, 22, 305, 306, 307, 308, 23, 309, 310, 24, 311, 25, 26, 312, 27, 313, 314, 315, 28, 316, 317, 318]

2001,

[319, 320, 321, 210, 29, 322, 30, 323, 324, 325, 326, 327, 328, 211, 329, 31, 330, 32, 33, 33, 331, 332, 34, 333, 334, 335, 336, 337, 338]

2002,

[339, 212, 340, 341, 342, 35, 343, 344, 345, 36, 346, 347, 348, 349, 350, 351, 352]

2003,

[353, 354, 355, 356, 357, 358, 213, 214]

2004,

[292, 359, 360, 361, 362, 363]

2005,

[364, 365, 366, 367, 215, 368, 369, 903]

2006,

[370, 371, 264]

2007,

[372]

Geographical index

4.9

31

Geographical index

The following table gives references to the contributions by country.

• Australia: [306, 410, 411, 457, 480, 485, 530, 564, 591, 632, 659, 670, 680, 727, 746, 753, 755, 319, 212, 344]

• Russia: [678, 710] • Singapore: [576]

• Austria: [70, 90, 587, 595, 597, 704] • Slovenia: [738, 216] • Belgium: [901, 200, 261, 262, 263, 265, 58, 423, 25] • South Korea: [447, 543, 572, 581, 584, 614, 693, 709, 734, 31]

• Brazil: [399, 462, 625] • Bulgaria: [226] • Canada: [193, 194, 195, 840, 779, 400, 436, 452, 506, 601, 729, 303, 331] • China: [460, 461, 464, 467, 487, 526, 537, 539, 540, 563, 565, 611, 658, 660, 716, 731, 760, 324, 213, 214, 899, 900, 406, 416, 426, 498, 577, 637, 744, 350]

• Spain: [21, 819, 450, 475, 79, 500, 501, 511, 574, 619, 644, 650, 662, 665, 706, 728, 739, 117, 293, 211, 329, 338] • Sweden: [228, 352] • Switzerland: [297]

• Cyprus: [174, 547]

• Taiwan: [382, 391, 439, 445, 536, 593, 607, 685, 686, 697, 698, 733, 735, 296, 314, 358]

• Denmark: [38]

• Thailand: [510]

• Egypt: [435, 325]

• The Czech Republic: [532, 699, 759, 533]

• Finland: [298, 877, 878, 221, 222, 208, 505, 525, 268, 225, 635, 674, 701, 723, 745, 292, 361, 903]

• The Netherlands: [859, 492, 98, 102, 108, 671, 708, 478]

• France: [777, 121, 136, 898, 259, 122, 260, 37, 42, 43, 388, 394, 428, 430, 68, 223, 459, 95, 608, 621, 684, 229, 115] • Germany: [340, 884, 885, 253, 886, 851, 196, 887, 772, 773, 147, 862, 888, 889, 801, 841, 258, 204, 438, 442, 71, 224, 496, 497, 499, 502, 508, 516, 623, 642, 648, 651, 675, 703, 748, 756, 757, 764, 316, 333, 336, 337, 339, 371]

771, 219, 519, 309,

• Greece: [538, 639, 114] • India: [552, 11, 707, 718, 762] • Italy: [238, 132, 133, 860, 790, 791, 134, 793, 135, 792, 804, 805, 861, 380, 387, 403, 415, 220, 444, 266, 477, 509, 517, 556, 610, 613, 617, 620, 657, 227, 717, 209, 307, 308, 320, 29, 332, 335] • Japan: [301, 302, 315, 346, 836, 896, 176, 201, 799, 831, 832, 167, 177, 895, 205, 902, 377, 392, 395, 51, 402, 53, 414, 420, 421, 425, 69, 451, 75, 456, 465, 466, 469, 470, 78, 473, 476, 479, 481, 482, 483, 486, 491, 504, 512, 86, 521, 528, 529, 544, 91, 550, 551, 559, 561, 562, 566, 573, 579, 580, 585, 588, 589, 590, 592, 596, 598, 600, 605, 624, 626, 629, 103, 647, 654, 661, 663, 668, 669, 673, 687, 691, 692, 696, 700, 111, 702, 712, 713, 714, 715, 721, 725, 732, 749, 754, 231, 119, 310, 312, 317, 326, 327, 334, 357, 368, 369, 372] • Latvia: [683] • Mexico: [518, 622, 676, 681] • New Zealand: [342] • Norway: [305]

• The Slovak Republic: [449] • United Kingdom: [36, 241, 239, 242, 243, 240, 244, 853, 800, 120, 13, 137, 138, 146, 165, 40, 384, 45, 49, 55, 57, 417, 61, 424, 64, 65, 431, 432, 67, 454, 463, 489, 82, 83, 84, 514, 527, 542, 545, 554, 557, 560, 94, 575, 582, 594, 97, 599, 612, 99, 630, 633, 101, 636, 638, 649, 653, 655, 105, 106, 109, 672, 677, 689, 112, 722, 730, 741, 116, 763, 300, 24, 26, 27, 323, 32, 33, 355, 356, 359, 360, 364, 366, 215, 370] • United States: [304, 23, 28, 874, 158, 833, 247, 148, 882, 145, 802, 803, 806, 156, 14, 821, 822, 881, 139, 248, 159, 823, 842, 186, 187, 807, 809, 160, 161, 834, 843, 16, 17, 254, 290, 188, 879, 271, 232, 273, 276, 149, 155, 810, 281, 282, 157, 285, 844, 181, 184, 189, 255, 880, 256, 774, 123, 140, 141, 808, 162, 163, 166, 250, 169, 848, 172, 179, 875, 190, 257, 207, 769, 775, 124, 272, 784, 233, 274, 786, 142, 279, 283, 811, 812, 164, 168, 845, 855, 868, 182, 872, 894, 202, 373, 125, 126, 787, 798, 143, 144, 150, 246, 813, 829, 838, 170, 171, 180, 869, 183, 892, 206, 374, 766, 767, 270, 778, 782, 234, 235, 788, 236, 151, 152, 153, 814, 815, 825, 826, 827, 835, 847, 857, 870, 871, 876, 191, 192, 891, 199, 897, 375, 765, 768, 770, 783, 785, 237, 275, 795, 277, 154, 280, 284, 816, 818, 824, 286, 839, 175, 856, 203, 291, 376, 39, 379, 381, 385, 44, 47, 389, 390, 393, 396, 397, 50, 52, 401, 404, 405, 407, 408, 409, 54, 412, 413, 56, 59, 60, 418, 217, 422, 218, 62, 427, 429, 66, 433, 434, 440, 441, 443, 72, 446, 453, 76, 458, 468, 471, 472, 474, 267, 80, 81, 484, 488, 490, 493, 494, 495, 503, 85, 513, 515, 87, 520, 522, 523, 524, 88, 89, 531, 535, 541, 546, 92, 553, 558, 567, 568, 570, 571, 578, 583, 96, 586, 602, 606, 609, 615, 616, 618, 627, 628, 634, 641, 645, 656, 664, 666, 667, 690, 230, 694, 695, 110, 113, 705, 269, 711, 719, 720, 724, 742, 743, 747, 750, 751, 118, 761, 294, 299, 22, 311, 313, 318, 321, 322, 30, 328, 35, 354, 362]

• Portugal: [548, 646, 752, 363]

• Unknown country: [850, 127, 378, 507, 549, 555, 569, 604, 640, 643, 652, 104, 107, 682, 726, 737, 740, 758, 295, 210, 330, 343, 348, 349, 351, 353, 367, 264]

• Romania: [73, 688, 736]

• Venezuela: [865, 603, 679]

• Poland: [77, 100, 345, 365]

32


Bibliography [1] John H. Holland. Genetic algorithms. Scientific American, 267(1):44–50, 1992. ga:Holland92a. [2] Jarmo T. Alander. An indexed bibliography of genetic algorithms: Years 1957-1993. Art of CAD Ltd., Vaasa (Finland), 1994. (over 3000 GA references). [3] David E. Goldberg, Kelsey Milman, and Christina Tidd. Genetic algorithms: A bibliography. IlliGAL Report 92008, University of Illinois at Urbana-Champaign, 1992. ga:Goldberg92f. [4] N. Saravanan and David B. Fogel. A bibliography of evolutionary computation & applications. Technical Report FAU-ME-93-100, Florida Atlantic University, Department of Mechanical Engineering, 1993. (available via anonymous ftp site magenta.me.fau.edu directory /pub/ep-list/bib file EC-ref.ps.Z) ga:Fogel93c. [5] Thomas B¨ ack. Genetic algorithms, evolutionary programming, and evolutionary strategies bibliographic database entries. (personal communication) ga:Back93bib, 1993. [6] Thomas B¨ ack, Frank Hoffmeister, and Hans-Paul Schwefel. Applications of evolutionary algorithms. Technical Report SYS-2/92, University of Dortmund, Department of Computer Science, 1992. ga:Schwefel92d. [7] David L. Hull. Uncle Sam wants you. Science, 284(5417):1131–1133, 14. May 1999. [8] Leslie Lamport. LATEX: A Document Preparation System. User’s Guide and Reference manual. AddisonWesley Publishing Company, Reading, MA, 2 edition, 1994. [9] Alfred V. Aho, Brian W. Kernighan, and Peter J. Weinberger. The AWK Programming Language. AddisonWesley Publishing Company, Reading, MA, 1988. [10] Diane Barlow Close, Arnold D. Robbins, Paul H. Rubin, and Richard Stallman. The GAWK Manual. Cambridge, MA, 0.15 edition, April 1993. [11] N. M. Hewahi and K. K. Bharadwaj. Bucket brigade algorithm for hierarchical censored production rule-based system. International Journal of Intelligent Systems, 11(4):197–225, 1996. †CCA48996/96 ga96bHewahi. [12] M. Compiani, D. Montanari, and R. Serra. Learning and bucket brigade dynamics in classifier systems. In Forrest [904]. † ga:Compiani90a. [13] Derek F. Yates and Andrew Fairley. An investigation into possible causes of, and solutions to, rule strength distortion in the bucket brigade. In Forrest [905], pages 246–253. ga:Fairley93b. [14] John H. Holland. Properties of the bucket brigade. In Grefenstette [906], pages 1–7. ga:Holland85a. [15] Dijia Huang. The context-array bucket-brigade algorithm: An enchanced approach to credit-apportionment in classifier systems. In Schaffer [907], pages 311–316. ga:Huang89. [16] Rick L. Riolo. Bucket brigade performance: I. long sequence of classifiers. In Grefenstette [908], pages 184–195. ga:Riolo87a. [17] Rick L. Riolo. Bucket brigade performance: II. default hierarchies. In Grefenstette [908], pages 196–201. ga:Riolo87b. [18] Thomas H. Westerdale. ga:Westerdale85.

The bucket brigade is not genetic.

In Grefenstette [906], pages 45–59.

[19] Thomas H. Westerdale. A defence of the bucket brigade. In Schaffer [907], pages 282–290. ga:Westerdale89. [20] Alwyn Barry. Specifying action persistence within XCS. pages 50–57, 2000. ga00aABarry. [21] Ester Bernad´ o i Mansilla and Josep Maria Garrell i Guiu. MOLeCS: A multiobjective learning classifier system. page 390, 2000. ga00aEBiMansilla.

33

34


[22] John H. Holmes, Dennis R. Durbin, and Flaura K. Winston. The learning classifier system: an evolutionary computation approach to knowledge discovery in epidemiologic surveillance. Artificial Intelligence in Medicine, 19(1):53–74, May 2000. ga00aJHHolmes. [23] Martin V. Butz, David E. Goldberg, and Wolfgang Stolzmann. Introducing a genetic generalization pressure to the anticipatory classifier system - Part 1: Theoretical approach. pages 34–41, 2000. ga00aMVButz. [24] R. E. Smith, B. A. Dike, R. K. Mehra, B. Ravichandran, and A. El-Fallah. Classifier systems in combat: two-sided learning of maneuvers for advanced fighter aircraft. Computer Methods in Applied Mechanics and Engineering, 186(2-4):421–437, 9. June 2000. ga00aRESmith. [25] S. Sette and L. Boullart. An implementation of genetic algorithms for rule based machine learning. AAAAAAA, ?(?), ? 2000. ga00aSSette. [26] Tim Kovacs. Strength or accuracy? Fitness calculation in learning classifier systems. In P. L. Lanzi, W. Stolzmann, and S. W. Wilson, editors, Learning Classifier Systems, From Foundations to Applications, volume LNAI of 1813, pages 143–160, ?, ? 2000. Springer-Verlag Berlin Heidelberg. * www /Springer ga00aTKovacs. [27] William N. L. Browne, Karen M. Holford, Carolynne J. Moore, and John Bullock. An industrial learning classifier system: the importance of preprocessing real data and choise alphabet. Engineering Applications of Artificial Intelligence, 13(?):25–36, ? 2000. ga00aWNLBrowne. [28] Martin V. Butz, David E. Goldberg, and Wolfgang Stolzmann. Introducing a genetic generalization pressure to the anticipatory classifier system - Part 2: Performance analysis. pages 42–49, 2000. ga00bMVButz. [29] Andrea Bonarini and Vito Trianni. Learning fuzzy classifier systems for multi-agent coordination. Information Sciences, 136(?):215–239, ? 2001. ga01aAndreaBonarini. [30] Chen-Lu Meng and Ramakrishnan Pakath. The iterated prisoner’s dilemma: early experiences with learning classifier system-based simple agents. Decision Support Systems, 31(?):379–403, ? 2001. ga01aChen-LuMeng. [31] Jongsoo Lee and Prabhat Hajela. Application of classifier system in improving response surface based approximations for design optimization. Computers & Structures, 79(?):333–344, ? 2001. ga01aJongsooLee. [32] K. Sirlantzis, M. C. Fairhurst, and M. S. Hoque. Genetic algorithms for multi-classifier system configuration: a case study in character recognition. In J. Kittler and F. Roli, editors, Multiple Classifier Systems, Second International Workshop, MCS 2001, volume LNCS of 2096, pages 99–108, Cambridge (UK), 2.-4. July 2001. Springer-Verlag Berlin Heidelberg. * www /Springer ga01aKSirlantzis. [33] L. Bull. On coevolutionary genetic algorithms. Soft Computing, 5(3):201–207, ? 2001. * www /Springer ga01aLBull. [34] Martin V. Butz. Anticipatory Learning Classifier Systems, volume 4 of Genetic Algorithms and Evolutionary Computation. Kluwer Academic Publishers, Boston, MA, 2001. †www ga01aMVButz. [35] John H. Holmes, Pier Luca Lanzi, Wolfgang Stolzmann, and Stewart W. Wilson. Learning classifier systems: New models, successful applications. Information Processing letters, 82(?):23–30, ? 2002. ga02aJHHolmes. [36] Luis Miramontes Hercog and Terence C. Fogarty. Social simulation using a multi-agent model based on classfier systems in ”El Farol” Bar problem. pages 77–82, 2002. ga02aLMHercog. [37] Philippe Andrey and Philippe Tarroux. Unsupervised image segmentation using a distributed genetic algorithm. Pattern Recognition, 27(5):659–673, May 1994. ga94aAndrey. [38] Peter Rolann Arentoft and Kaj Aage Jensen. Simuleret skovbrandsbekæmpelse – et eksempel p˚ a genetisk baseret maskinindlæring [Simulated forest fire fights – an example of genetic based machine learning]. Report DAIMI IR-120, Aarhus University, Computer Science Department, 1994. (in Danish) ga94aArentoft. [39] Haldun Aytug, Gary J. Koehler, and Jane L. Snowdon. Genetic learning of dynamic scheduling within a simulation environment. Computers & Operations Research, 21(8):909–925, October 1994. ga94aAytug. [40] Lawrence Bull and Terence C. Fogarty. Parallel evolution of communicating classifier systems. In ICEC’94 [909], pages 680–685. ga94aBull. [41] Brian Carse. A fuzzy classifier system using the Pittsburgh approach. In Davidor et al. [910], page ? †conf. prog. ga94aCarse. [42] Philippe Collard, Cathy Escazut, and Jean-Philippe Aurand. A polymorphic genetic algorithm for classifier systems. In EUFIT’94 [911], pages 1331–1332. ga94aCollard. [43] Jean-Yves Donnart and Jean-Arcady Meyer. A hierarchical classifier system implementing a motivationally autonomous adaptive animat. In David Cliff, Philip Husbands, and Jean-Arcady Meyer, editors, From Animals to Animats 3. Proceedings of the Third International Conference on Simulation of Adaptive Behavior, page ?, ?, ? 1994. MIT Press, Cambridge, MA. †Meyer ga94aDonnart.

Bibliography

35

[44] Garett Dwormann. Games computers play: simulating characteristic function game playing agents with classifier systems. In ICEC’94 [909], pages 696–701. ga94aDwormann. [45] Andrew Fairley and Derek F. Yates. Inductive operators and rule repair in a hybrid genetic learning system: some initial results. In Terence C. Fogarty, editor, Evolutionary Computing. Selected Papers of the AISB Workshop, pages 166–179, Leeds (UK), 11.-14. April 1994. Springer-Verlag, Berlin. * CCA 17040/95 ga94aFairley. [46] S. Finnerty and S. Sen. Simulated annealing based classification. In ?, editor, Proceedings of the 6th IEEE Conference on Tools with Artificial Intelligence (TAI’94), pages 824–827, New Orleans, LA, 6.-9. November 1994. IEEE Computer Society Press, Los Alamitos, CA. †CCA 241/95 ga94aFinnerty. [47] Murray Gell-Mann. ga94aGell-Mann.

The Quark and the Jaguar.

W. H. Freeman and Company, New York, 1994.

[48] Uwe Hartmann. On the complexity of learning in classifier systems. In Davidor et al. [910], page ? †conf. prog. ga94aHartmann. [49] S. Hedges and A. Hunter. Convergence and the classifier system. In ?, editor, Proceedings of the 7th Australian Joint Conference on Artificial Intelligence (AI’94), pages 307–313, Armidale, NSW (Australia), 21.-25. November 1994. World Scientific, Singapore. * CCA 61186/96 ga94aHedges. [50] Alastair D. McAulay and Jae Chan Oh. Improving learning of genetic rule-based classifier systems. IEEE Transactions on Systems, Man, and Cybernetics, 24(1):152–159, January 1994. ga94aMcAulay. [51] Norihiko Ono, T. Kataoka, and Adel Torkaman Rahmani. Evolutionary organization of cooperative strategies in learning classifier system organisms. In Proceedings of the IASTED International Conference: Modelling, Simulation and Identification, pages 237–240, Wakayama, Japan, 12.-16. September 1994. IASTED, Calgary, Alta., Canada. †EEA15865/97 ga94aNOno. [52] K. Nakaoka, Takeshi Furuhashi, and Yoshiki Uchikawa. A study on apportionment of credits of fuzzy classifier system for knowledge acquisition of large scale systems. In FuzzyIEEE94 [912], pages –. ga94aNakaoka. [53] Takao Terano and Zen ichirou Muro. On-the-fly knowledge base refinement by a classifier system. AI Communications, 7(2):86–97, June 1994. ga94aTerano. [54] Stewart W. Wilson. ZCS: A zeroth level classifier system. Evolutionary Computation, 2(1):1–18, Spring 1994. ga94aWilson. [55] Derek F. Yates and Andrew Fairley. Evolutionary stability in simple classifier systems. In Terence C. Fogarty, editor, Evolutionary Computing. Selected Papers of the AISB Workshop, pages 28–37, Leeds (UK), 11.-14. April 1994. Springer-Verlag, Berlin. * CCA 12508/95 ga94aYates. [56] Lawrence Bull and Terence C. Fogarty. Evolving cooperative communicating classifier systems. In Sebald and Fogel [913], pages 308–315. †conf.prog ga94bBull. [57] Brian Carse and Terence C. Fogarty. A delayed-action classifier system for learning in temporal environments. In ICEC’94 [909], pages 670–673. ga94bCarse. [58] Marco Dorigo and Hugues Bersini. A comparison of q-learning and classifier systems. In ?, editor, Proceedings of the Third International Conference on Simulation of Adaptive Behavior, pages 248–255, Brighton, UK, 8.-12. August 1994. MIT Press, Cambridge, MA. * CCA34553/96 ga94bDorigo. [59] Jeffrey Horn, David E. Goldberg, and Kalyanmoy Deb. Implicit niching in a learning classifier system: Nauture’s way. Technical Report IlliGAL report No. 94001, University of Illinois at Urbana-Champaign, 1994. also as [66] †IlliGAL ga94bHorn. [60] Chris Nikolopoulos and Paul Fellrath. Hybrid expert system for investment advising. Expert Systems, 11(4):245–248, November 1994. * EI M061853/94 ga94bNikolopoulos. [61] Brian Carse. Learning anticipatory behaviour using a delayed action classifier system. In Fogarty [914], pages 210–223. †Fogarty ga94cCarse. [62] Chris Nikolopoulos. Using classifier systems to design neural nets. In Proceedings of the International Conference on Tools with Artificial Intelligence, pages 812–815, New Orlearns, LA, 6.-9.November 1994. IEEE Computer Society Press, Los Alamitos, CA. †CCA 2744/95 ga94cNikolopoulos. [63] Gerhard Weiß. Hierarchical chunking in classifier systems. In Proceedings of the Tweftth National Conference on Artificial Intelligence, volume 2, pages 1335–1340, Seattle, WA, 31. July-4. August 1994. AAAI Press / The MIT Press. ga94cWeiss. [64] Brian Carse. Learning anticipatory behaviour using a delayed action classifier system. In Terence C. Fogarty, editor, Evolutionary Computing. Selected Papers of the AISB Workshop, pages 210–223, Leeds (UK), 11.-14. April 1994. Springer-Verlag, Berlin. * CCA 17041/95 ga94dCarse.

36


[65] Terence C. Fogarty, Brian Carse, and Lawrence Bull. Classifier systems – recent research. AISB Quarterly, ?(89):48–54, ? 1994. †Fogarty ga94dFogarty. [66] Jeffrey Horn, David E. Goldberg, and Kalyanmoy Deb. Implicit niching in a learning classifier system: Nauture’s way. Evolutionary Computation, 2(1):37–66, August 1994. also as [59] †IlliGAL ga94dHorn. [67] Lawrence Bull and Terence C. Fogarty. Evolution in multi-agent systems: Evolving communicating classifier systems for gait in a quadrapedal robot. In Eshelman [915], page ? †prog ga95aBull. [68] Philippe Collard and Cathy Escazut. DCS: A promising classifier system. In Pearson et al. [916], pages 14–17. ga95aCollard. [69] Takeshi Furuhashi, K. Nakaoka, Kojima Morikawa, H. Maeda, and Yoshiki Uchikawa. A study on knowledge finding using fuzzy classifier system. Jpn. J. Fuzzy Theory Syst. (USA), 7(4):555–567, 1995. †CCA51971/96 ga95aFuruhashi. [70] Andreas Geyer-Schulz. Holland classifier systems. APL Quote Quad, 25(4):43–55, June 1995. (Proceedings of the International Conference on APL, June 4.-8., 1995, San Antonio, TX) ga95aGeyer-Schulz. [71] Saman K. Halgamuge and Manfred Glesner. Practical handbook of genetic algorithms. In Chambers [917], chapter 14. Input space segmentation with a genetic algorithm for generation of rule based classifier systems, pages 317–331. ga95aHalgamuge. [72] Jianhua Lin. Adaptive image quantization based on learning classifier systems. In Proceedings of the 5th Data Compression Conference, page 477, Snowbird, UT, 28.-30. March 1995. IEEE, Piscataway, NJ. * EI M122090/95 ga95aJLin. [73] Arpad Kelemen, Maria Imecs, Calin Rusu, and Zoltan Kis. Run-time autotuning of a robot controller using a genetics based machine learning control scheme. In IEE/IEEE Sheffield ’95 [918], pages 307–312. ga95aKelemen. [74] J. Neves and M. Santos. A distributed computational environment for genetic-based classifier systems. In Xin Yao, editor, Proceedings of the Eight Australian Joint Conference on Artificial Intelligence, pages 467–474, ?, ? 1995. World Scientific Publishers, Co., Singapore. †News /Yao ga95aNeves. [75] J. Ohwi, S.V. Ulyanov, and K. Yamafuji. GA in continuous space and fuzzy classifier system for opening of door with manipulator of mobile robot: new benchmark of evolutionary intelligent computing. In ICEC’95 [919], pages 257–261. †prog. ga95aOhwi. [76] Robert A. Richards. Zeroth-Order Shape Optimization Utilizing a Learning Classifier System. PhD thesis, Stanford University, 1995. †[93] ga95aRichards. [77] F. Seredynski, P. Cichosz, and G. P. Klebus. Learning classifier systems in multi-agent environments. In IEE/IEEE Sheffield ’95 [918], pages 287–292. †conf.prog ga95aSeredynski. [78] Hisao Ishibuchi, Tomoharu Nakashima, and Tadahiko Murata. A fuzzy classifier system that generates fuzzy if-then rules for pattern classification problems. In ICEC’95 [919], pages 759–764. †prog. ga95bIshibuchi. [79] Jorge Muruzabal. Fuzzy and probabilistic reasoning in simple learning classifier systems. In ICEC’95 [919], pages 262–266. †prog. ga95bMuruzabal. [80] S. Sen and M. Sekaran. Multiagent coordination with learning classifier systems. In ?, editor, Proceedings of the Adaption and Learning in Multi-Agent Systems, pages 218–233, Montreal, Que (Canada), 21. August 1995. Springer-Verlag, Berlin (Germany). †CCA34494/96 ga95bSSen. [81] Patrick Tufts. Dynamic classifiers: genetic programming and classifier systems. In Proceedings of the Genetic Programming. Papers from the 1995 AAAI Fall Symposium, pages 114–119, Cambridge, MA (USA), 10.12. November 1995. AAAI Press, Menlo Park, CA. †CCA52032/96 ga95bTufts. [82] Terence C. Fogarty, Lawrence Bull, and Brian Carse. Evolving multi-agent systems. In Winter et al. [920], pages 1–22. ga95hFogarty. [83] Lawrence Bull and Terence C. Fogarty. Evolutionary computing in multi-agent environments: speciation and symbiogenesis. In Voigt et al. [921], pages 12–21. ga96aBull. [84] Brian Carse and Terence C. Fogarty. Evolutionary learning of controllers using temporal fuzzy classifier systems. In Parmee and Denham [922], page ? †conf.prog ga96aCarse. [85] H. Brown Cribbs III and Robert E. Smith. Classifier system renaissance: New analogies, new directions. In Koza et al. [923], page ? †conf.prog ga96aCribbs. [86] Satoshi Endoh, Hiroshi Kawakami, and Azuma Ohuchi. Extension of hierarchically classifier system using self GA invocation. In Proceedings of the 1996 IEEE International Conference on Systems, Man and Cybernetics, volume 4, pages 2534–2539, Beijing, China, 14.-17. October 1996. IEEE, Piscataway, NJ. †EI M041801/97 ga96aEndoh.

Bibliography

37

[87] Murray Gell-Mann. Kvarkki ja jaguaari, pages 370–373. Werner S¨ oderstr¨ om Osakeyhti¨ o, Juva (Finland), 1996. (translation of [47]; in Finnish) ga96aGell-Mann. [88] Jeffrey Horn and David E. Goldberg. Natural niching for cooperative learning in classifier systems. In Koza et al. [923], page ? †conf.prog ga96aHorn. [89] John H. Holmes. Evolution-Assisted Discovery of Sentinel Features in Epidemiologic Surveillance. PhD thesis, Drexel University, 1996. †[35] ga96aJHHolmes. [90] Johann Mitl¨ ohner. Classifier systems and economic modeling. APL Quote Quad, 26(4):77–86, June 1996. (Proceedings of the APL96 Conference) ga96aMitlohner. [91] K. Nakaoka, T. Furuhashi, Y. Uchikawa, and H. Maeda. A proposal on payoffs and apportionment of credits of fuzzy classifier system - finding of knowledge for large scale systems. Jpn. J. Fuzzy Theory Syst. (USA), 8(1):25–34, 1996. †CCA34346/97 ga96aNakaoka. [92] Robert A. Richards and Sheri D. Sheppard. A learning classifier system for three-dimensional shape optimization. In Voigt et al. [921], pages 1032–1042. ga96aRRRichards. [93] Robert A. Richards and Sheri D. Sheppard. Three-dimensional shape optimization utilizing a learning classifier system. In Koza et al. [923], page ? †conf.prog ga96aRichards. [94] Tim Kovacs. Evolving optimal populations with XCS classifier systems. Research Papers CSRP-96-17, The University of Birmingham, School of Computer Science, 1996. †[109] ga96aTimKovacs. [95] Gilles Venturini. A classifier system approach to control in changing environments. In Parmee and Denham [922], page ? †conf.prog ga96aVenturini. [96] Sandip Sen. Modeling human categorization by a simple classifier system. In Proceedings of the First Online Workshop on Soft Computing (WSC1), pages 71–76, WWW (World Wide Web), 19.-30. August 1996. Nagoya University. ga96bSandipSen. [97] Brian Carse, Terence C. Fogarty, and A. Munro. Distributed adaptive routing control in communication networks using a temporal fuzzy classifier system. In Proceedings of the Fifth IEEE International Conference on Fuzzy classifier system, volume 3, pages 2201–2207, New Orleans, LA, 8.-11. September 1996. IEEE, New York, NY. †EEA118170/96 ga96eCarse. [98] Antoine H. C. van Kampen and Lutgarde M. C. Buydens. Reinvestigation of a genetic-based classifier system: the effectiveness of recombination. Computers Chem. (Oxford), 21(3):153–160, 1997. ga97AHCvanKampen. [99] D. G. Esp and Kevin Warwick. Artificial Intelligence Techniques in Power Systems, volume 22 of IEE Power Engineering Series, chapter 5. Alarm analysis, pages 87–108. IEE, London, 1997. ga97aDGEsp. [100] Franciszek Seredy´ nski. Task scheduling with use of classifier systems. In Proceedings of the Evolutionary Computing, pages 287–306, Manchester (UK), 7.-8. April 1997. Springer-Verlag, Berlin (Germany). †CCA12408/98 ga97aFSeredynski. [101] K. Chalk and George D. Smith. Multi-agent classifier systems and the iterated prisoners dilemma. In George D. Smith and Nigel C. Steele, editors, Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pages 615–618, Norwich, UK, 2.-4. April 1997. ga97aKChalk. [102] Antoine H. C. van Kampen. The Applicability of Genetic Algorithms to Complex Optimization Problems in Chemistry. PhD thesis, Katholieke Universiteit Nijmegen, 1997. ga97aKampen. [103] Ichiro Nagasaka and Toshiharu Taura. Geometric representation for shape generation using classifier system. In Koza et al. [924], page ? †conf.prog ga97aNagasaka. [104] Bryan G. Spohn and Philip H. Crowley. Complexity of strategies and the evolution of cooperation. In Koza et al. [924], page ? †conf.prog ga97aSpohn. [105] Tim Kovacs. XCS classifier system reliably evolves accurate, complete, and minimal representations for Boolean functions. In Roy, Chawdhry, and Pant, editors, Soft Computing in Engineering Design and Manufacturing, volume ?, pages 59–68, ?, ? 1997. Springer-Verlag, Berlin. * ResearchIndex ga97aTimKovacs. [106] W. Browne, K. Holford, C. Moore, and J. Bullock. A practical application of a learning classifier system in a steel hot strip mill. In George D. Smith and Nigel C. Steele, editors, Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pages 611–614, Norwich, UK, 2.-4. April 1997. ga97aWBrowne. [107] Thonmas H. Westerdale. Classifier systems – no wonder they don’t work. In Koza et al. [924], page ? †conf.prog ga97aWesterdale.

38


[108] Antoine H. C. van Kampen, Z. Ramadan, M. Mulholland, D. B. Hibbert, and Lutgarde M. C. Buydens. Learning classification rules from an ion chromatography database using a genetic algorithm-based classifier system. Analytica Chimica Acta, 344(1-2):1–15, 30. May 1997. ga97bKampen. [109] Tim Kovacs. XCS classifier system reliably evolves accurate, complete, and minimal representations for Boolean functions. Research Papers CSRP-97-19, The University of Birmingham, School of Computer Science, 1997. ga97bTimKovacs. [110] Jim Levenick. Showing the way: a review of the second edition of Holland’s adaptation in natural and artificial systems. Artificial Intelligence, 100(1-2):331–338, April 1998. ga98aJimLevenick. [111] Makoto Fujii and Takeshi Furuhashi. A proposal of human-machine interactive system through linguistic instructions using fuzzy classifier system. In ?, editor, Proceedings of the 3rd International Symposium on Artificial Life and Robotics (AROBIII’98), volume 2, pages 532–536, ?, ? 1998. ? (available via www URL: http://www.bioele.nuee.nagoya-u.ac.jp/member/furuhashi/publications.html#Bf1990) ga98aMakotoFujii. [112] Michael O. Odetayo. Generating rules using a Holland based classifier learning system. In Oˇsmera [925], pages 80–85. ga98aOdetayo. [113] Prabhat Hajela and B. Kim. Classifier systems for enhancing neural network-based global function approximations. In Proceedings of the 7th AIAA/USAF/NASA/ISSMO Symposium on Multisciplinary Analysis and Optimization, pages 371–380, St. Louis, MO, 2.-4. September 1998. American Institute of Aeronautics and Astronautics, Reston, VA. †A98-39739 ga98aPHajela. [114] A. Stafylopatis and K. Blekas. Autonomous vehicle navigation using evolutionary reinforcement learning. European Journal of Operational Research, 108(2):306–318, 16. July 1998. ga98aStafylopatis. [115] G. Enee and Cathy Escazut. Classifier systems evolving multi-agent system with distributed elitism. In Proceedings of the 1999 Congress on Evolutionary Computation-CEC99, volume 3, pages 1740–1746, Washington, DC, 6.-9. July 1999. IEEE, Piscataway, NJ. †CCA83615/99 ga99aGEnee. [116] T. Polichroniadis and N. Dodgson. Motion blending using a classifier system. In Proceedings of the 7th International Conference in Central Europe on Computer Graphics, volume 1, pages 225–232, Plzen (Czech Republic), 8.-12. February 1999. Univ. West Bohemia, Plzen (Czech Republic). †CCA73416/99 ga99aPolichro. [117] A. Sanchis, J. M. Molina, P. Isasi, and J. Segovia. Knowledge acquisition including tags in a classifier system. In Proceedings of the 1999 Congress on Evolutionary Computation-CEC99, volume 1, pages 137– 144, Washington D.C., 6.-9. July 1999. IEEE, Piscataway, NJ. †CCA77371/99 ga99aSanchis. [118] Franciszek Seredy´ nski and C. Z. Janikow. Learning Nash equilibria by coevolving distributed classifier systems. In Proceedings of the 1999 Congress on Evolutionary Computation-CEC99, volume 3, pages 1619– 1626, Washington D.C., 6.-9. July 1999. IEEE, Piscataway, NJ. †CCA83601/99 ga99aSeredynski. [119] Hsiao Ishobuchi and Tomoharu Nakashima. Improving the performance of fuzzy classifier systems for pattern classification problems with continuous attributes. IEEE Transactions on Industrial Electronics, 46(6):1057–1068, December 1999. ga99bHIshibuchi. [120] A. M. Barry. The emergency of high level structure in classifier systems. Irish Journal of Psychology, 14(3):480–498, ? 1993. †Fogarty ga:Barry93a. [121] Pierre Bonelli and Alexandre Parodi. An efficient classifier system and its experimental comparison with two representative learning methods on three medical domains. In Belew and Booker [926], pages 288–295. ga:Bonelli91. [122] Alexandre Parodi and Pierre Bonelli. A new approach to fuzzy classifier systems. In Forrest [905], pages 223–230. ga:Bonelli93a. [123] Lashon B. Booker, David E. Goldberg, and John H. Holland. Classifier systems and genetic algorithms. Artificial Intelligence, 40(1-3):235–282, September 1989. ga:Booker89a. [124] Lashon B. Booker. Using classifier systems to implement distributed representations. In Maureen Caudill, editor, International Joint Conference on Neural Networks, (IJCN-90-WASH-DC), volume 1, pages I–39–I– 42, Washington, DC, 15.-19. January 1990. Lawrence Erlbaum Assoc. Publ., Hillsdale, NJ. ga:Booker90a. [125] Lashon B. Booker. Representing attribute-based concepts in a classifier system. In Rawlins [927], pages 115–127. ga:Booker91a. [126] Lashon B. Booker. Instinct as an inductive bias for learning behavioral sequences. In Meyer and Wilson [928], pages 230–237. ga:Booker91b.

Bibliography

39

[127] Antonella Carbonaro, Giorgio Casadei, and Aldopaolo Palareti. Genetic algorithms and classifier systems in simulating a cooperative behaviour. In Albrecht et al. [929], pages 479–483. ga:Casadei93b. [128] Philippe Collard. Back specialization with the bucket brigade algorithm. In Proceedings of the 10th IASTED Symposium on Applied Informatics, pages 130–133, Innsbruck, Austria, 10.-12. February 1992. ACTA Press, Anaheim, CA. † ga:Collard92. [129] M. Compiani, D. Montanari, R. Serra, and G. Valastro. Classifier systems and neural networks. In E. R. Caianiello, editor, First Italian workshop: Parallel architectures and neural networks, pages 105–118, Teaneck, NJ, ? 1989. World Scientific. † ga:Compiani89b. [130] M. Compiani, R. Serra, and P. Simonini. Dynamical systems in artificial intelligence: The case of classifier systems. In R. Pfeifer, Z. Schreter, F. Fogelmann-Soulie, and L. Stees, editors, Connectionism in 9 Perspective, pages 331–340, ?, ? 1989. Elsevier Science Publishers, New York. † ga:Compiani89c. [131] M. Compiani, D. Montanari, and R. Serra. Dynamics of classifier systems. In ?, editor, Proceedings of the 2nd Workshop on Parallel Architectures and Neural Networks, volume ?, page ?, ?, ? 1992. ? (in press) † ga:Compiani92. [132] Marco Dorigo and Uwe Schnepf. Organisation of robot behaviour through genetic learning process. In Proceedings of the Fifth International Conference on Advanced Robotics, Robots in Unstructured Environments, volume 2, pages 1456–1460, Pisa (Italy), 19.-22. June 1991. IEEE Press. ga:Dorigo91f. [133] Marco Dorigo. New perspectives about default hierarchies formation in learning classifier systems. In E. Ardizzone, S. Gaglio, and F. Sorbello, editors, Proceedings of the II Italian Congress on Artificial Intelligence, volume ?, pages 218–227, Palermo, ? 1991. Springer-Verlag, Berlin. †Bersini ga:Dorigo91h. [134] Marco Dorigo. Alecsys and the autonomouse: Learning to control a real robot by distributed classifier systems. Technical Report 92-011, Politecnico di Milano, Dipartimento di Elettronica, 1992. ga:Dorigo92f. [135] Marco Dorigo and Uwe Schnepf. Genetics-based machine learning and behaviour based robotics: A new synthesis. IEEE Transactions on Systems, Man, and Cybernetics, 23(1):141–154, 1993. ga:Dorigo93b. [136] Renaud Dumeur. Extended classifiers for simulation of adaptive behavior. In Meyer and Wilson [928], pages 58–63. ga:Dumeur91a. [137] Lawrence Bull and Terence C. Fogarty. Co-evolving communicating classifier systems for tracking. In Albrecht et al. [929], pages 522–527. ga:Fogarty93a. [138] Terence C. Fogarty. Classifier systems for control. In Proceedings of the IEE Colloquium on Genetic Algorithms for Control and Systems Engineering, volume Digest No. 1993/130, pages 8/1–8/3, London, 28. May 1993. IEE, London. †Fogarty ga:Fogarty93c. [139] Stephanie Forrest. The classifier system: A computational model that supports machine intelligence. In Proceedings of the 1986 International Conference on Parallel Processing, pages 711–716, ?, ? 1986. ? † ga:Forrest86a. [140] Stephanie Forrest. Classifier systems as dynamical systems. Technical Report Center for Nonlinear Studies newsletter LALP-89-04, Los Alamos National Laboratory, 1989. † ga:Forrest89a. [141] Stephanie Forrest and John H. Miller. The dynamics of classifier systems: Empirical results. Technical Report No. LA-UR 89-3287, Los Alamos National Laboratory, 1989. † ga:Forrest89b. [142] Stephanie Forrest and John H. Miller. Emergent behaviour in classifier systems. In Forrest [904], pages 213–227. ga:Forrest90b. [143] Stephanie Forrest. Parallelism and Programming in Classifier Systems. ga:Forrest91book2.

Pittman, 1991.

†[930]

[144] Stephanie Forrest. Parallelism in classifier systems. Technical Report No. CNLS-MS-B258, Los Alamos National Laboratory, 1991. † ga:Forrest91d. [145] Stephanie Forrest. A study of parallelism and programming in classifier systems and its application to classification in KL-ONE semantic networks. PhD thesis, University of Michigan, Ann Arbor, 1985. * DAI 46/7 ga:ForrestThesis. [146] Gary Roberts. Dynamic planning for classifier systems. In Forrest [905], pages 231–237. ga:GRoberts93a. [147] Gerhard Weiß. Learning the goal relevance of actions in classifier systems. In Bernd Neumann, editor, ECAI 92 10th European Conference on Artificial Intelligence, pages 430–434, Vienna (Austria), 3.-7. August 1992. John Wiley & Sons. †Fogel/bib ga:GWeiss92a. [148] David E. Goldberg. Computer-aided pipeline operation using genetic algorithms and rule learning. In ?, editor, Proceedings of the API Pipeline Cybernetics Symposium, page ?, ?, May 9. 1984. ? ga:Goldberg84a.

40


[149] Abdollah Homaifar, David E. Goldberg, and C. C. Carroll. Boolean function learning with a classifier system. In Proceedings of SPIE’s 1988 Technical Symposium on Optics, Electro-Optics, and Sensors, volume SPIE-937, pages 264–272, Orlando, FL, 4.-6. April 1988. SPIE. ga:Goldberg88b. [150] Robert Elliot Smith and David E. Goldberg. Variable default hierarchy separation in a classifier system. In Rawlins [927], pages 148–167. ga:Goldberg91n. [151] David E. Goldberg, Jeffrey Horn, and Kalyanmoy Deb. What makes a problem hard for a classifier system? IlliGAL Report 92007, University of Illinois at Urbana-Champaign, 1992. also as [152] †IlliGAL ga:Goldberg92ed. [152] David E. Goldberg, Jeffrey Horn, and Kalyanmoy Deb. What makes a problem hard for a classifier system? In ?, editor, Collected Abstracts for the First International Conference on Learning Classifier Systems, page ?, Houston, TX, 6.-8. October 1992. ? also as [151] †IlliGAL ga:Goldberg92ee. [153] Robert Elliot Smith and David E. Goldberg. Reinforcement learning with classifier systems: Adaptive default hierarchy formation. Applied Artificial Intelligence, 6(1):79–102, 1992. (also TCGA Report No. 90002)* ga:Goldberg92h. [154] Lashon B. Booker and David E. Goldberg. Classifier systems and genetic algorithms. In B. G. Buchanan and D. C. Wilkins, editors, Readings in Knowledge Acquisition and Learning: Automating the Construction and Improvement of Expert Systems, pages 812–836. Morgan Kaufmann Publishers Inc., San Mateo, CA, 1993. †ACM/93 ga:Goldberg93h. [155] M. Goodloe and S. J. Graves. Improving performance of an electrical-power expert system with genetic algorithms. In M. Ali, editor, Proceedings: The First International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA/AIE-88), pages 298–305, University of Tennessee, Space Institute, Tullahoma, TN, 1.-3. June 1988. ACM, New York. * P40550 EEA 43161/91 ga:Graves88a. [156] Hayong (Harry) Zhou. Classifier systems with long term memory. In Grefenstette [906], pages 178–182. ga:HZhou85. [157] Hayong (Harry) Zhou. A study of automatic rule generation and adaptation. In Proceedings of the First International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, volume 2, pages 747–753, Tullahoma, TN, ? 1988. ACM Press, New York. ga:HZhou88a. [158] John H. Holland. A more detailed discussion of classifier systems. Technical report year 1983?, University of Michigan, Ann Arbor, Department of Computer and Communication Sciences, 1981. † ga:Holland83a. [159] John H. Holland. ga:Holland86b.

A mathematical framework for studying learning classifier systems.

1986.

†

[160] John H. Holland, Keith J. Holyoak, Richard E. Nisbett, and Paul R. Thagard. Classifier systems, Qmorphisms, and induction. In Lawrence Davis, editor, Genetic Algorithms and Simulated Annealing, pages 116–128. Pitman Publishing, London, 1987. ga:Holland87a. [161] John H. Holland. Genetic algorithms and classifier systems: foundations and future directions. In Grefenstette [908], pages 82–89. ga:Holland87b. [162] John H. Holland and A. W. Burks. Method of controlling a classifier system, 1989. (U.S. Patent 4,881,178. Issued Nov. 14 1989) † ga:Holland89a. [163] John H. Holland. Using classifier systems to study adaptive nonlinear networks. In D. Stein, editor, Lectures in the Science of Complexity, SFI Studies in the Science of Complexity. Addison-Wesley, Reading, MA, 1989. † ga:Holland89b. [164] John H. Holland. Concerning the emergence of tag-mediated lookahead in classifier systems. In Forrest [904], pages 188–210. † ga:Holland90a. [165] L. Kiernan and Kevin Warwick. Adaptive alarm processor for fault diagnosis on power transmission networks. Intelligent Systems Engineering, 2(1):25–37, 1993. * ga:KWarwick93b. [166] Charles L. Karr, Donald A. Stanley, and B. J. Schneiner. Automation and optimization of froth flotation. In ?, editor, Proceedings of the Annual Invitation Symposium, Space Mining and Manufacturing UA/NASA Space Engineering Research Center for Utilization of Local Planetary Resources, volume IV, pages IV–44– IV–58, Tucson, AZ, 24.-26. October 1989. NASA? ga:Karr89a. [167] Takashi Kawakami and Yukinori Kakazu. Study on an autonomous robot navigation problem using a classifier system. Nippon Kikai Gakkai Ronbunshu C Hen, 59(564):2339–2345, August 1993. * EI Feb 94 ga:Kawakami93a.

Bibliography

41

[168] Lori A. Wang. Classifier system learning of the Boolean multiplexer function. Master’s thesis, University of Tennessee, Knoxville, Department of Computer Science, 1990. † ga:LAWangMSThesis. [169] M. R. Hilliard, Gunar E. Liepins, G. Rangarajan, and Mark R. Palmer. Alternatives for classifier system credit assignment. In N. S. Sridharan, editor, Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), pages 756–761, Detroit, MI, 20.-25. August 1989. Morgan Kaufmann, Palo Alto, CA. † ga:Liepins89d. [170] Gunar E. Liepins and Lori A. Wang. Classifier system learning of Boolean concepts. In Belew and Booker [926], pages 318–323. ga:Liepins91a. [171] Gunar E. Liepins and M. R. Hilliard. Credit assignment and discovery in classifier systems. International Journal of Intelligent Systems, 6(?):55–69, 1991. † ga:Liepins91d. [172] Alastair D. McAulay and Jae Chan Oh. Image learning classifier system using genetic algorithms. In Proceedings of the IEEE 1989 National Aerospace and Electronics Conference (NAECON 1989), pages 705–713, Dayton, OH, 22.-26. May 1989. IEEE, New York. ga:McAulay89. [173] Alastair D. McAulay and Jae Chan Oh. Improved learning in genetic rule-based classifier systems. In Proceedings of the 1991 IEEE International Conference on Systems, Man and Cybernetics, volume 2, pages 1393–1398, Charlottesville, VA, 13.-16. October 1991. IEEE, New York. * EI A083878/92 ga:McAulay91b. [174] N. Panayides, C. N. Schizas, C. S. Pattichis, A. Theodosius, and L. T. Middleton. Genetics-based-machinelearning in clinical electromyography. In Teuvo Kohonen, Kai M¨ akisara, Olli Simula, and Jari Kangas, editors, Artificial Neural Networks, Proceedings of the 1991 International Conference on Artificial Neural Networks (ICANN-91), volume 2, pages 1625–1628, Espoo (Finland), 24.-28. June 1991. North-Holland. ga:Middleton91. [175] Jim R. Oliver. Discovering individual decision rules: an application of genetic algorithms. In Forrest [905], pages 216–222. ga:Oliver93a. [176] Adel Torkaman Rahmani and Norihiko Ono. Genetic evolution of communication in distributed classifier systems. In ?, editor, Proceedings of the 45th National Conference of Information Processing Society of Japan, page ?, ?, ? 1992. ? † ga:Ono92a. [177] Norihiko Ono and Adel Torkaman Rahmani. Self-organization of communication in distributed learning classifier systems. In Albrecht et al. [929], pages 361–367. ga:Ono93a. [178] Robert A. Richards and Sheri D. Sheppard. Learning classifier systems in design optimization. Design Theory and Methodology, DE-42(?):179–186, ? 1992. †[931] ga:RARichards92a. [179] Robert Elliot Smith and Manuel Valenzuela-Rend´ on. A study of rule set development in a classifier system. In Schaffer [907], pages 340–346. ga:RESmith89. [180] Robert Elliot Smith. Default hierarchy formation and memory exploitation in learning classifier systems. PhD thesis, University of Alabama, 1991. (also TCGA report No. 91003) ga:RESmith91thesis. [181] Rick L. Riolo. CFS-C: A package of domain-independent subroutines for implementing classifier systems in arbitrary, user-defined environments. Technical Report ?, University of Michigan, Department of Computer Science and Engineering, Logic of Computers Group, 1988. †[930] ga:Riolo88a. [182] Rick L. Riolo. Lookahead planning and latent learning in a classifier system. In Meyer and Wilson [928], pages 316–326. ga:Riolo90a. [183] Rick L. Riolo. Modeling simple human category learning with a classifier system. In Belew and Booker [926], pages 324–333. ga:Riolo91. [184] Rick L. Riolo. Empirical studies of default hierarchies and sequences of rules in learning classifier systems. PhD thesis, University of Michigan, Department of Computer Science and Engineering, 1988. (University Microfilms No. 89-07143) †[93] [930] ga:RioloThesis. [185] George G. Robertson. Population size in classifier systems. In Proceedings of the Fifth International Conference on Machine Learning, pages 142–152, ?, ? 1988. ? † ga:Robertson88a. [186] Stewart W. Wilson. Classifier systems learning of a Boolean function. Technical Report Research Memo RIS-27r, The Rowland Institute for Science, Cambridge, MA, 1986. † ga:SWWilson86a. [187] Stewart W. Wilson. Classifier systems and the Animat problem. Technical Report Research Memo RIS-36r, The Rowland Institute for Science, Cambridge, MA, 1986. † ga:SWWilson86b. [188] Stewart W. Wilson. Hierarchical credit allocation in a classifier system. In ?, editor, Proceedings of the Tenth International Joint Conference on Artificial Intelligence, pages 217–220, ?, ? 1987. Morgan Kaufman, Los Altos, CA. †Wilson ga:SWWilson87e.

42


[189] Stewart W. Wilson. Bid competition and specificity reconsidered. Complex Systems, 2(6):705–723, ? 1988. †Dorigo91aa ga:SWWilson88a. [190] Stewart W. Wilson. Hierarchical credit allocation in a classifier system. In M. S. Elzas, T. I. Oren, and B. P. Zeigler, editors, Modelling and simulation methodology, Knowledge Systems’ Paradigms, page ?, Tucson, AZ, 19.-23. January 1989. North-Holland, Amsterdam. †Wilson ga:SWWilson89b. [191] Stewart W. Wilson. Classifier system mapping of real vectors [extended abstract]. In ?, editor, Collected Abstracts for the First International Conference on Learning Classifier Systems, page ?, Houston, TX, 6.-8. October 1992. ? †Wilson ga:SWWilson92a. [192] Stewart W. Wilson. Toward a GA solution of the discovery problem [extended abstract]. In ?, editor, Collected Abstracts for the First International Conference on Learning Classifier Systems, page ?, Houston, TX, 6.-8. October 1992. ? †Wilson ga:SWWilson92b. [193] Dale Schuurmans and Jonathan Schaeffer. Representational difficulties with classifier systems. In Schaffer [907], pages 328–333. ga:Schaeffer89a. [194] Lingyan Shu and Jonathan Schaeffer. VCS: Variable classifier systems. In Schaffer [907], pages 334–339. ga:Schaeffer89b. [195] Lingyan Shu and Jonathan Schaeffer. HCS: Adding hierarchies to classifier systems. In Belew and Booker [926], pages 339–345. ga:Schaeffer91a. [196] Mukesh J. Patel and Uwe Schnepf. Concept formation as emergent phenomena. In Varela and Bourgine [932], pages 11–20. ga:Schnepf91a. [197] P. A. Schrodt. Short-term prediction of international behavior using a Holland classifier. Mathematical and Computer Modelling, 12(4/5):589–600, 1989. † ga:Schrodt89a. [198] Dale Schuurmans, R. Chai, and Lingyan Shu. Learning using classifier systems: A survey. In Proceedings of the CIRP Intelligent Control Conference, pages 386–391, ?, ? 1987. ? † ga:Schuurmans87. [199] Michael M. Skolnick. A classifier system for learning spatial representations, based on a Pebble-Pond morphological wave propagation algorithm. In ?, editor, Visual Communications and Image Processing ’92, volume SPIE-1818, pages 919–930, Boston, MA, 18. -20. November 1992. The International Society for Optical Engineering. * EEA 62209/93 ga:Skolnick92a. [200] Piet Spiessens. PCS: A classifier system that builds a predictive internal world model. In Luigia Carlucci Aiello, editor, ECAI 90 9th European Conference on Artificial Intelligence, pages 622–627, Stockholm, 6.-10. August 1990. Pitman Publishing, London. ga:Spiessens90b. [201] Shozo Tokinaga and Andrew B. Whinston. Applying adaptive credit assignment algorithm for the learning classifier system based upon the genetic algorithm. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, E75-A(5):568–577, May 1992. ga:Tokinaga92a. [202] Kirk E. Twardowski. Implementation of genetic algorithm based associative classifier system (ACS). In Apostolos Dollas and Nikolaos G. Bourbakis, editors, Proceedings of the 1990 IEEE International Conference on Tools with Artificial Intelligence TAI’90, pages 48–54, Herndon, VA, 6.-9. November 1990. IEEE Computer Society Press, Los Alamitos, CA. †EI A083877/92 ga:Twardowski90a. [203] Kirk E. Twardowski. Credit assignment for pole balancing with learning classifier systems. In Forrest [905], pages 238–245. ga:Twardowski93a. [204] Uwe Hartmann. Efficient parallel learning in classifier systems. In Albrecht et al. [929], pages 515–521. ga:UHartmann93a. [205] Kojima Morikawa, Takeshi Furuhashi, and Yoshiki Uchikawa. Controlling excessive fuzzyness in a fuzzy classifier system. In Forrest [905]. ga:Uchikawa93a. [206] Manuel Valenzuela-Rend´ on. The fuzzy classifier system: A classifier system for continuously varying variables. In Belew and Booker [926], pages 346–353. ga:Valenzuela91a. [207] Manuel Valenzuela-Rend´ on. Two analysis tools to describe the operation of classifier systems. PhD thesis, University of Alabama, Tuscaloosa, 1989. (Also TCGA report No. 89005) † ga:ValenzuelaThesis. [208] Jarmo T. Alander. Indexed bibliography of learning classifier systems. Report 94-1-LCS, University of Vaasa, Department of Information Technology and Production Economics, 1995. (available via anonymous ftp site ftp.uwasa.fi directory cs/report94-1 file gaLCSbib.ps.Z) gaLCSbib. [209] Filippo Neri. Mining TCP/IP traffic for network intrusion detection by using a distributed genetic algorithm. In Ramon L´ opez de M´ antaras and Enric Plaza, editors, Machine Learning: ECML 200, Proceedings of the 11th European Conference on Machine Learning, volume 1810, pages 313–322, Barcelona (Spain), 31. May2. June 2000. Springer-Verlag, Berlin. ga00aFilippoNeri.

Bibliography

43

[210] A. Papagelis and D. Kalles. Breeding decision trees using evolutionary techniques. In ?, editor, Proceedings of the 18th International Conference on Machine Learning, pages 393–400, ?, ? 2001. Morgan Kaufmann, San Mateo. †[933] ga01aAPapagelis. [211] José L. Sanz-Gonz´ alez and Diego Andina. Importance sampling techniques in neural detector training. In L. De Raedt and P. Flach, editors, Machine Learning: EMCL 2001, 12th European Conference on Machine Learning, volume LNAI of 2167, pages 431–441, Freiburg (Germany), 5.-7. September 2001. Springer-Verlag Berlin Heidelberg. * www /Springer ga01aJLSanz-Gonzalez. [212] Honghua Dai, Gang Li, and Yiqing Tu. An empirical study of encoding schemes and search strategies in discovering causal networks. In Tapio Elomaa, Heikki Mannila, and H. Toivonen, editors, Proceedings of the ECML 2002, 13th European Conference on Machine Learning, volume 2430 of Lecture Notes in Computer Science, pages 48–59, Helsinki (Finland), 19.-23. August 2002. Springer-Verlag, Heidelberg. ga02aHonghuaDai. [213] Ying Li, Yan-Ning Zhang, Rong-Chun Zhao, and Li-Cheng Jiao. A new method for edge detection. In Proceedings of the 2003 IEEE International Conference on Machine Learning and Cybernetics, volume 3, pages 1780–1784, ?, 2.-5. November 2003. IEEE, Piscataway, NJ. ga03aYingLi. [214] Jun an Yang, Hui Peng, and Zhenquan Zhuang. Research of nonlinear blind source separation algorithm based on quantum evolutionary neural network. In Proceedings of the 2003 IEEE International Conference on Machine Learning and Cybernetics, volume 2, pages 835–840, Xi’an (China), 2.-5. November 2003. IEEE, Piscataway, NJ. ga03bJun-anYang. [215] M. Roberts and Ela Claridge. Learning feature extraction and classification simultaneously by means of co-evolutionary genetic programming. In ?, editor, BMVA Symposium on Pattern Recognition in Machine Learning in Machine Vision, volume ?, page ?, London (UK), January 2005. ? †www /Claridge ga05aMRoberts. [216] S. Dzeroski and I. Petrovski. Discovering dynamics with genetic programming. In ?, editor, Proceedings of the European Conference on Machine Learning (ECML-94), pages 347–350, Catania (Italy), 6.-8. April 1994. Springer-Verlag, Berlin. * CCA 11902/95 ga94aDzeroski. [217] Haleh Vafaie and Kenneth A. De Jong. Improving a rule learning system using genetic algorithms. In ?, editor, Machine Learning: A Multistrategy Approach, pages 453–470. Morgan Kaufmann, ?, 1994. †GA /George Mason U ga94bVafaie. [218] John J. Grefenstette. An evolutionary approach to learning in robots. In ?, editor, Machine Learning Workshop on Robot Learning, page ?, New Brunswick, NJ, ? 1994. ? †GA /George Mason U ga94cGrefenstette. [219] J¨ urgen Branke, Udo Kohlmorgen, and Hartmut Schmeck. A distributed genetic algorithm improving the generalization behaviour of neural networks. In Nada Lavra¸c and Stefan Wrobel, editors, Proceedings of the 8th European Conference on Machine Learning (ECML-95), volume 914 of Lecture Notes in Artificial Intelligence, pages 107–121, Heraclion (Greece), 25.-27. April 1995. Springer-Verlag, Berlin. ga95aBranke. [220] Laura Caponetti, Fabio Abbattista, and G. Carapella. Boundary detection using genetic algorithms. In Proceedings of the Machine Learning and Perception, pages 149–156, Ancona (Italy), 21.-23. June 1995. World Scientific, Singapore. †CCA4169/97 ga95aCaponetti. [221] Jukka Hekanaho. Symbiosis in multimodal concept learning. In Armand Prieditis and Stuart Russell, editors, Proceedings of the Twefth International Conference on Machine Learning, pages 278–285, Tahoe City, CA, 9.-12. July 1995. ? ga95aHekanaho. [222] David E. Moriarty and Risto Miikkulainen. Efficient learning from delayed rewars through symbiotic evolution. In ?, editor, Proceedings of the Twelfth International Conference on Machine Learning, volume ?, pages 396–404, Tahoe City, CA (USA), 9.-12. July 1995. Morgan Kaufmann Publishers, San Francisco, CA (USA). †CCA40917/97 ga95aMoriarty. [223] Michele Sebag, Marc Schoenauer, and Caroline Ravise. An induction-based control for genetic algorithms. In Nada Lavra¸c and Stefan Wrobel, editors, Proceedings of the 8th European Conference on Machine Learning (ECML-95), volume 914 of Lecture Notes in Artificial Intelligence, pages 351–355, Heraclion (Greece), 25.-27. April 1995. Springer-Verlag, Berlin. ga95aSebag. [224] Peter Nordin, Frank D. Francone, and Wolfgang Banzhaf. Explicitly defined introns and destructive crossover in genetic programming. In ?, editor, Proceedings of the 12th International Conference on Machine Learning, GP Workshop, number 95.2, pages 6–22, Tahoe City, ? 1995. University of Rochester. (also as [934]) ga95ccNordin.

44


[225] Jukka Hekanaho. Background knowledge in GA-based concept learning. In Lorenza Saitta, editor, Proceedings of the 13th International Conference on Machine Learning (ICML’96), pages 234–242, Bari (Italy), 3.-6. July 1996. Morgan Kaufmann Publishers, San Francisco, CA. ga96bHekanaho. [226] N. I. Nikolaev and V. Slavov. Inductive genetic programming with decision trees. In Proceedings of the 9th European Conference on Machine Learning, pages 183–190, Prague, Czech Republic, 23.-25. April 1997. Springer-Verlag, Berlin (Germany). †EEA81769/97 ga97aNikolaev. [227] C. Anglano, A. Giordana, G. Lo Bello, and L. Saitta. Coevolutionary, distributed search for inducing concept descriptions. In Proceedings of the 10th European Conference on Machine Learning, pages 322–333, Chemnitz, Germany, 21.-23. April 1998. Springer-Verlag, Berlin (Germany). †CCA65896/98 ga98aAnglano. [228] Bj¨ orn Olsson. A host-parasite genetic algorithm for asymmetric tasks. In Proceedings of the 10th European Conference on Machine Learning, pages 346–351, Chemnitz, Germany, 21.-23. April 1998. Springer-Verlag, Berlin (Germany). †CCA65898/98 ga98aBOlsson. [229] A. Ducoulombier and M. Sebag. Continuous mimetic evolution. In Proceedings of the 10th European Conference on Machine Learning, pages 334–345, Chemnitz, Germany, 21.-23. April 1998. Springer-Verlag, Berlin (Germany). †CCA65897/98 ga98aDucoulom. [230] Hugues Juillé and Jordan B. Pollack. Coevolutionary learning: a case study. In Proceedings of the Fifteenth International Conference on Machine Learning, pages 251–259, Madison, WI, 24.-27. July 1998. Morgan Kaufmann Publishers, San Francisco, CA (USA). ga98aHJuille. [231] Yasubumi Sakakibara and Mitsuhiro Kondo. GA-based learning of context-free grammars using tabular representation. In Ivan Bratko and Saˇso Dˇzeroski, editors, Machine Learning, Proceedings of the Sixteenth International Conference (ICML’99), pages 354–360, Bled (Slovenia), 27.-30. June 1999. Morgan Kaufmann Publishers, San Francisco, CA. ga99aYasubumiSakakibara. [232] Lawrence Davis and D. K. Young. Classifier systems with Hamming weigths. In J. Laird, editor, Proceedings of the 5th International Conference on Machine Learning, pages 162–173, ?, ? 1988. Morgan Kaufmann, San Mateo. †Back/bib/unp ga:Davis88a. [233] Kenneth A. De Jong. Genetic-algorithm based learning. In Y. Kodratoff and R. Michalski, editors, Machine Learning, volume III, pages 611–638. Morgan Kaufmann, Los Altos, CA, 1990. † ga:DeJong90c. [234] Jerzy W. Bala, Kenneth A. De Jong, and Peter W. Pachowicz. Integrated inductive learning and and genetic algorithms for texture recognition. In Proceedings of the 10th International Conference on Machine Learning, page ?, Aberdeen, Scotland, July 1992. ga:DeJong92a. [235] Jerzy W. Bala, Kenneth A. De Jong, and Peter W. Pachowicz. Multistrategy learning form engineering data by integrating inductive generalization and genetic algorithms. In Ryszard S. Michalski and Gheorghe Tecuci, editors, Machine Learning: A Multistrategy Approach, volume IV, chapter X. Morgan Kaufmann: San Mateo, 1992. ga:DeJong92b. [236] John J. Grefenstette, Kenneth A. De Jong, and William M. Spears. Competition-based learning. In Alan Meyrowitz and Susan Chipman, editors, Foundations of Knowledge Acquisition: Machine Learning, page ? Kluwer, 1992. †Spears ga:DeJong92h. [237] William M. Spears, Kenneth A. De Jong, Thomas B¨ ack, David B. Fogel, and Hugo de Garis. An overview of evolutionary computation. In P. B. Brazil, editor, Proceedings of the European Conference on Machine Learning (ECML-93), volume 667 of Lecture Notes in Artificial Intelligence, pages 442–459, Vienna (Austria), 5.-7. April 1993. Springer-Verlag. ga:DeJong93b. [238] Marco Dorigo. Message-based bucket brigade: An algorithm for the apportionment of credit problem. In Y. Kodratoff, editor, Machine Learning - EWLS-91, European Working Session on Learning, Lecture Notes in Artificial Intelligence No. 482, pages 235–244, Porto (Portugal), 6.-8. March 1991. Springer-Verlag. ga:Dorigo91aa. [239] Terence C. Fogarty. An incremental genetic algorithm for real-time learning. In Proceedings of the Sixth International Workshop on Machine Learning, pages 416–419, ?, ? 1989. Morgan Kaufman Publishers, Inc. †Nang93b ga:Fogarty89d. [240] Terence C. Fogarty. Machines can learn actions but can they classify situations? In IEE Colloquium on Machine Learning, volume Digest No. 117, pages 4/1–4/3, London, 28. June 1990. IEE, London. †Fogarty ga:Fogarty90d. [241] Richard S. Forsyth. Evolutionary learning strategies. In Richard S. Forsyth and Roy Rada, editors, Machine Learning Applications in Expert Systems and Information Retrieval, pages 78–95. Ellis Horwood, Ltd., Chichester (UK), 1986. ga:Forsyth86.

Bibliography

45

[242] Richard S. Forsyth. Chapter 4. the evolution of intelligence. In Richard S. Forsyth, editor, Machine Learning, pages 65–82. Chapman and Hall, New York, 1989. † ga:Forsyth89a. [243] Richard S. Forsyth. ? In Richard S. Forsyth, editor, Machine Learning, pages 83–103. Chapman and Hall, New York, 1989. †[6] ga:Forsyth89b. [244] Richard S. Forsyth. Neural learning algorithms: some empirical trials. In IEE Colloquium on ‘Machine Learning’, volume IEE Digest No. 1990/?, pages 8/1–8/7, London, ? 1990. IEE, London. ga:Forsyth90a. [245] A. Giordana and C. Sale. Learning structured concepts using genetic algorithms. In Derek Sleeman and Peter Edwards, editors, Machine Learning, Proceedings of the Ninth International Workshop (ML92), pages 169–178. Morgan Kaufmann Publishers?, July 1992. † ga:Giordana92a. [246] Helen G. Cobb and John J. Grefenstette. Learning the persistence of actions in reactive control rules. In Lawrence A. Birnbaum and Gregg C. Collins, editors, Machine Learning, Proceedings of the Eighth International Workshop (ML91), pages 293–297. Morgan Kaufmann: San Mateo, 1991. ga:Grefenstette91b. [247] John H. Holland and J. S. Reitman. Cognitive systems based on adaptive algorithms. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, editors, Machine Learning: An Artificial Intelligence Approach, page ? Morgan Kaufmann, Los Altos, CA, 1983. †Michalewicz92book ga:Holland83b. [248] John H. Holland. Escaping brittleness: The possibilities of general purpose learning algorithms applied to parallel rule-based systems. In R. S. Michalski and J. G. Carbonell, editors, Machine Learning II, pages 593–623. Morgan Kaufmann, Los Altos, CA, 1986. † ga:Holland86a. [249] Nagesh Kadaba and Kendall E. Nygard. Improving the performance of genetic algorithms in automated discovery of parameters. In Bruce Porter and Raymond Mooney, editors, Machine Learning: Proceedings of the Seventh International Conference, pages 140–148, University of Texas, 21. - 23. June 1990. Morgan Kaufmann Publishers, Inc. ga:Kadaba90. [250] M. R. Hilliard, Gunar E. Liepins, G. Rangarajan, and Mark R. Palmer. Learning decision rules for scheduling problems. In Proceedings of the Sixth International Workshop on Machine Learning, pages 188–200, Cornell University, Ithaca, NY, June 1989. Morgan Kauffman, San Mateo, CA. † ga:Liepins89b. [251] Bernard Manderick. The genetic algorithm. In Walter Daelemans and David Powers, editors, Background and Experiments in Machine Learning of Natural Language, Proceedings of the First SHOE Workshop, pages 53–115, Tilburg (Netherlands), May 1992. Institute for Language Technology and AI Tilburg University. ga:Manderick92. [252] R. Andrew McCallum and Kent A. Spackman. Using genetic algorithms to learn disjunctive rules from examples. In Bruce Porter and Raymond Mooney, editors, Machine Learning: Proceedings of the Seventh International Conference, pages 149–152, University of Texas, 21. - 23. June 1990. Morgan Kaufmann Publishers, Inc. ga:McCallum90. [253] Ingo Rechenberg. Artificial evolution and artificial intelligence. In Richard S. Forsyth, editor, Machine Learning: Principles and Techniques, pages 83–103. Chapman and Hall, London, 1989. †[935] ga:Rechenberg89a. [254] Stewart W. Wilson. Quasi-Darwinian learning in a classifier system. In Pat Langley, editor, Proceedings of the Fourth International Workshop on Machine Learning, pages 59–65, University of California, Irvine, 22. - 25. June 1987. Morgan Kaufmann Publishers, Inc. ga:SWWilson87b. [255] II Adrian V. Sannier. Midgard: A genetic approach to adaptive load balancing for distributed systems. In Proceedings of the Fifth International Conference on Machine Learning, pages 174–180, ?, ? 1988. † ga:Sannier88a. [256] Richard A. Caruana and J. David Schaffer. Representation and hidden bias: Gray vs. binary coding for genetic algorithms. In Proceedings of the Fifth International Conference on Machine Learning, pages 153– 162, ?, ? 1988. Morgan Kaufmann, Los Altos, CA. † ga:Schaffer88a. [257] Richard A. Caruana, J. David Schaffer, and Larry J. Eshelman. Using multiple representations to improve inductive bias - Gray and binary coding for genetic algorithms. In A. M. Segre, editor, Proceedings of the Sixth International Workshop on Machine Learning, pages 375–378, Cornell University, Ithaca, NY, June 1989. Morgan Kauffman, San Mateo, CA. †P42485 ga:Schaffer89c. [258] Steffen Schulze-Kremer. Genetic algorithms for protein tertiary structure prediction. In P. B. Brazil, editor, Proceedings of the European Conference on Machine Learning (ECML-93), volume 667 of Lecture Notes in Artificial Intelligence, pages 262–279, Vienna (Austria), 5.-7. April 1993. Springer-Verlag. †CCA 22653/93 ga:Schulze-K93b.

46


[259] Gilles Venturini. Agil: Solving the exploration versus exploitation dilemma in a simple classifier system applied to simulated robotics. In Derek Sleeman and Peter Edwards, editors, Machine Learning, Proceedings of the Ninth International Workshop (ML92), pages 458–463. Morgan Kaufmann Publishers?, July 1992. ga:Venturini92a. [260] Gilles Venturini. SIA: a supervised inductive algorithm with genetic search for learning attributes based concepts. In P. B. Brazil, editor, Proceedings of the European Conference on Machine Learning (ECML-93), volume 667 of Lecture Notes in Artificial Intelligence, pages 280–296, Vienna (Austria), 5.-7. April 1993. Springer-Verlag. †Venturini ga:Venturini93a. [261] Hugo de Garis. Building artificial nervous systems using genetically programmed neural network modules. In Bruce Porter and Raymond Mooney, editors, Machine Learning: Proceedings of the Seventh International Conference, pages 132–139, University of Texas, 21. - 23. June 1990. Morgan Kaufmann Publishers, Inc. ga:deGaris90a. [262] Hugo de Garis. Genetic programming: Building artificial nervous systems using genetically programmed neural network modules. In Proceedings of the 7th International Conference on Machine Learning, pages 132–139, ?, ? 1990. Morgan Kaufmann. † ga:deGaris90e. [263] Hugo de Garis. Genetic programming: Evolutionary approaches to multistrategy learning. In Ryszard S. Michalski and Gheorghe Tecuci, editors, Machine Learning: A Multistrategy Approach, volume IV, chapter 21. Morgan Kaufmann: San Mateo, 1992. † ga:deGaris92b. [264] S. Whiteson and P. Stone. Evolutionary function approximation for reinforcement learning. Journal of Machine Learning Research, 7(?):877–917, ? 2006. †[936] ga06aSWhiteson. [265] Marco Dorigo. Alecsys and the autonomouse. Machine Learning, ?(?):?, 1994. (to appear) ga94aDorigo. [266] Marco Dorigo. Alecsys and the autonomouse: learning to control a real robot by distributed classifier systems. Machine Learning, 19(3):209–240, 1995. †EI M146832/95 ga95bDorigo. [267] Rebecca J. Parsons, Stephanie Forrest, and Christian Burks. Genetic algorithms operators, and DNA fragment assembly. Machine Learning, 21(1-2):11–33, October/November 1995. ga95bParsons. [268] David E. Moriarty and Risto Miikkulainen. Efficient reinforcement learning through synbiotic evolution. Machine Learning, 22(1-3):11–32, January/February/March 1996. ga96aMoriarty. [269] Jordan B. Pollack and Alan D. Blair. Co-evolution in the successful learning of backgammon strategy. Machine Learning, 32(3):225–240, September 1998. * www /DEMO ga98aPollack. [270] Nicol N. Schraudolph and Richard K. Belew. Dynamic parameter encoding for genetic algorithms. Machine Learning, 9(1):9–21, June 1992. ga:Belew92b. [271] Lashon B. Booker. Classifier systems that learn internal world models. Machine Learning, 3(2/3):161–192, October 1988. ga:Booker88a. [272] Lawrence Davis. Classifier systems with Hamming weights. Machine Learning, 5(?):162–173, 1990. ga:Davis90a.

†

[273] Kenneth A. De Jong. Learning with genetic algorithms: An overview. Machine Learning, 3(2/3):121–138, 1988. †[935] ga:DeJong88a. [274] Kenneth A. De Jong. Genetic algorithms. Machine Learning, 5(4):351–353, October 1990. † ga:DeJong90d. [275] Kenneth A. De Jong, William M. Spears, and Diana F. Gordon. Using genetic algorithms for concept learning. Machine Learning, 13(2-3):161–188, November-December 1993. ga:DeJong93d. [276] Richard K. Belew and Stephanie Forrest. Learning and programming in classifier systems. Machine Learning, 3(2/3):193–224, October 1988. † ga:Forrest88a. [277] Stephanie Forrest and Melanie Mitchell. What makes a problem hard for a genetic algorithm? Some anomalous results and their explanation. Machine Learning, 13(2-3):285–319, November-December 1993. (available via anonymous ftp site ftp.santafe.edu directory /pub/Users/mm file Forrest-Mitchell-ML.ps.Z) ga:Forrest93h. [278] P. W. Frey and D. J. Slate. Letter recognition using Holland-style adaptive classifiers. Machine Learning, 6(?):161–182, 1991. † ga:Frey91. [279] David E. Goldberg. Probability matching, the magnitude of reinforcement, and classifier system bidding. Machine Learning, 5:407–425, 1990. (also TCGA Report No. 88002) ga:Goldberg90d. [280] David Perry Greene and Stephen F. Smith. Competition-based induction of decision models from examples. Machine Learning, 13(2-3):229–257, November-December 1993. ga:Greene93a.

Bibliography

47

[281] J. Michael Fitzpatrick and John J. Grefenstette. Genetic algorithms in noisy environments. Machine Learning, 3(2/3):101–120, October 1988. † ga:Grefenstette88b. [282] John J. Grefenstette. Credit assignment in rule discovery systems based on genetic algorithms. Machine Learning, 3(2/3):225–246, October 1988. † ga:Grefenstette88c. [283] John J. Grefenstette, Connie Loggia Ramsey, and Alan C. Schultz. Learning sequential decision rules using simulation models and competition. Machine Learning, 5(4):355–381, 1990. ga:Grefenstette90a. [284] John J. Grefenstette. Special issue on genetic algorithms. Machine Learning, 13(2/3):157–319, November/December 1993. ga:Grefenstette93e. [285] David E. Goldberg and John H. Holland. Genetic algorithms and machine learning. Machine Learning, 3(?):95–99, 1988. ga:Holland88a. [286] Cezary Z. Janikow. A knowledge-intensive genetic algorithm for supervised learning. Machine Learning, 13(2-3):189–228, November-December 1993. ga:Janikow93a. [287] J. R. Quinlan. An empirical comparison of genetic and decision-tree classifiers. Machine Learning, 5(?):135– 141, 1990. † ga:Quinlan90a. [288] George G. Robertson and Rick L. Riolo. A tale of two classifier systems. Machine Learning, 3(2/3):139–160, October 1988. † ga:Robertson88b. [289] George G. Robertson. Population size in classifier systems. Machine Learning, 5(?):142–152, 1990. ga:Robertson90a.

†

[290] Stewart W. Wilson. Classifier systems and the Animat problem. Machine Learning, 2(3):199–228, 1987. † ga:SWWilson87d. [291] Darrell Whitley, Stephen Dominic, Rajarshi Das, and C. W. Anderson. Genetic reinforcement learning for neurocontrol problems. Machine Learning, 13(2-3):259–284, 1993. ga:Whitley93d. [292] Janne Haverinen. Utilizing the fundamental effect of space in creating purposeful dynamic systems. In Jarmo T. Alander, Pekka Ala-Siuru, and Heikki Hy¨ otyniemi, editors, STeP-2004, Proceedings of the 11th Finnish Artificial Intelligence Conference, volume 3, pages 137–150, Vantaa (Finland), 1.-3. September 2004. Finnish Artificial Intelligence Society (FAIS). (also available via www URL: ) STeP04Haverinen. [293] Aleix M. Mart´ınez and Jordi Vitri` a. Learning mixture models using a genetic version of the EM algorithm. Pattern Recognition Letters, 21(8):759–769, July 2000. ga00aAMMartinez. [294] Astro Teller and Manuela Veloso. Internal reinforcement in a connectionist genetic programming approach. Artificial Intelligence, 120(2):165–198, July 2000. ga00aATeller. [295] Benoit. Biologically plausible learning rules for neural networks and quantum computing. Neurocomputing, 32-33(?):921–926, June 2000. †www /Elsevier ga00aBenoit. [296] Chia-Feng Juang, Jiann-Yow Lin, and Chin-Teng Lin. Genetic reinforcement learning through symbiotic evolution for fuzzy controller design. IEEE Transactions on Systems, Man, and Cybernetics, 30(2):290–302, April 2000. ga00aC-FJuang. [297] Dario Floreano and Joseba Urzelai. Evolutionary robots with on-line self-organization and behavioral fitness. Neural Networks, 13(4-5):431–443, May/June 2000. ga00aDFloreano. [298] Daniel Polani and Risto Miikkulainen. Eugenic neuro-evolution for reinforcement learning. pages 1041–1046, 2000. ga00aDPolani. [299] Gary B. Parker. Co-evolving model parameters for anytime learning in evolutionary robotics. Robotics and Autonomous Systems, 33(?):13–30, ? 2000. ga00aGaryBParker. [300] H. Hagras, V. Callaghan, and M. Colley. Online learning of fuzzy behavior coordination for autonomous agents using genetic algorithms and real-time interaction with the environment. In 9th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2000), volume 1-2, pages 853–858, San Antonio, TX, USA, 7.-10.May 2000. IEEE, New York. †P89705 ga00aHHagras. [301] Hisao Ishibuchi and Tomoharu Nakashima. Linguistic rule extraction by genetics-based machine learning. pages 195–202, 2000. ga00aHisaoIshibuchi. [302] Hitoshi Iba and Makoto Terao. Controlling effective introns for multi-agent learning by genetic programming. pages 419–426, 2000. ga00aHitoshiIba. [303] Ivan Bruha, Pavel Kralik, and Petr Berka. Genetic learner: Discretization and fuzzification of numerical attributes. Intelligent Data Analysis, 4(5):445–460, ? 2000. * http://iospress.metapress.com ga00aIBruha.

48


[304] Jalal Baghdadchi. A classifier based learning model for intelligent agents. page 870, 2000. ga00aJBaghdadchi. [305] Jim Torresen. Scalable evolvable hardware applied to road image recognition. In Jason Lohn, Adrian Stoica, Didier Keymeulen, and Silvano Colombano, editors, Proceedings of the Second NASA/DoD Workshop on Evolvanle Hardware, pages 245–252, Palo Alto, CA, 13.-15. July 2000. IEEE Computer Society. ga00aJimTorresen. [306] Luigi Barone and Lyndon While. Adaptive learning for poker. pages 566–574, 2000. ga00aLBarone. [307] L. Glielmo. A machine learning approach to modelling and identification of automotive. IEEE/ASME Transactions on Mechatronics, 5(2):132–141, June 2000. * www/IEEE ga00aLGlielmo. [308] Marco Russo. Genetic fuzzy learning. IEEE Transactions on Evolutionary Computation, 4(?):259–273, ? 2000. †[335] ga00aMRusso. [309] Michael H¨ usken, Jens E. Gayko, and Bernhard Sendhoff. Optimization for problem classes - neural networks that learn to learn. In Xin Yao and David F. Fogel, editors, Proceedings of the 2000 IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks (ECNN 2000), pages 98–109, San Antonio, TX, ? 2000. IEEE Press, New York, NY. ga00aMichaelHusken. [310] Moritoshi Yasunaga, Taro Nakamura, Jung H. Kim, and Ikuo Yoshihara. Kernel-based pattern recognition hardware: its design methology using evolved truth tables. In Jason Lohn, Adrian Stoica, Didier Keymeulen, and Silvano Colombano, editors, Proceedings of the Second NASA/DoD Workshop on Evolvanle Hardware, pages 253–262, Palo Alto, CA, 13.-15. July 2000. IEEE Computer Society. ga00aMoritoshiYasunaga. [311] Robert E. Smith and Lia N. Tusanotte Cisnero. Genetic reinforcement learning in a robotic system for space exploration. In The Case for Mars V; Proceedings of the 5th Conference, Boulder, CO, 26.-29. May 1993 2000. Univelt Inc., San Diego, CA. AAS Paper 93-837 * A00-26731 ga00aRobertESmith. [312] Utomo Sarjono Putro, Kyoichi Kijima, and Shingo Takahashi. Adaptive learning of hypergame situations using a genetic algorithm. IEEE Transactions on Systems, Man, and Cybernetics, 30(5):562–572, September 2000. ga00aUSPutro. [313] Alan C. Schultz and John J. Grefenstette. Continuous and embedded learning in autonomous vehicles: adapting to sensor failures. In Grant R. Gerhart, Robert W. Gunderson, and Chuck M. Shoemaker, editors, Unmanned Ground Vehicle Technology II, volume SPIE-4024, pages 55–62, ?, July 2000. The International Society for Optical Engineering. * www/SPIE Web ga00bACSchultz. [314] Chin-Teng Lin and Chong-Ping Jou. GA-based fuzzy reinforcement learning for control of a magnetic bearing system. IEEE Transactions on Systems, Man, and Cybernetics, 30(2):276–289, April 2000. ga00bC-TLin. [315] Isao Ono, Tetsuo Nijo, and Noriko Ono. A genetic algorithm for automatically designing modular reinforcement learning agents. pages 203–210, 2000. ga00bIOno. [316] Michael H¨ usken and Christian Goerick. Fast learning for problem classes using knowledge based network initialization. In Sun-Ichi Amari, C. Lee Giles, Marco Gori, and Vincenzo Piuri, editors, Proceedings of the International Joint Conference on Neural Networks (IJNN 2000), volume VI, pages 619–624, Como (Italy), ? 2000. IEEE Computer Society Press, Los Alamitos, CA. ga00bMichaelHusken. [317] Yong Liu, Xin Yao, and Tetsuya Higuchi. Evolutionary ensembles with negative correlation learning. IEEE Transactions on Evolutionary Computation, 4(4):380–387, November 2000. ga00bYLiu. [318] Richard A. Watson, Sevan G. Ficici, and Jordan B. Pollack. Embodied evolution: Distributing an evolutionary algorithm in a population of robots. Technical Report CS-00-208, Brandeis University, Volen Center for Complex Systems, 2000. ga00cRAWatson. [319] Chi-Hsu Wang, Han-Leih Liu, and Chin-Teng Lin. Dynamic optimal learning rates of a certain class of fuzzy neural networks and its applications with genetic algorithm. IEEE Transactions on Systems, Man and Cybernetics - Part B: Cybernetics, 31(3):467–475, June 2001. ga01C-HWang. [320] Andrea Bonarini. Evolutionary learning, reinforcement learning, and fuzzy rules for knowledge acquisition in agent-based systems. Proceedings of the IEEE, 89(9):1334–1346, September 2001. ga01aABonarini. [321] Arturo Hern´ andez Aguirre, Bill P. Buckles, and Carlos A. Coello Coello. GA-based learning of kDNFn S Boolean formulas. In Y. Liu, K. Tanaka, M. Iwata, T. Higuchi, and M. Yasunaga, editors, Evolvable Systems: From Biology to Hardware, 4th International Conference, ICES 2001, volume LNCS of 2210, pages 279–290, Tokyo (Japan), 3.-5. October 2001. Springer-Verlag Berlin Heidelberg. * www /Springer ga01aAHAquirre. [322] Barry K. Lavine, D. Brzozowski, Anthony J. Moores, C. E. Davidson, and Howard T. Mayfield. Genetic algorithm for fuel spill identification. Analytica Chimica Acta, 437(2):233–246, 27. June 2001. ga01aBarryKLavine.

Bibliography

49

[323] David I. Ellis and Royston Goodacre. Rapid and quantitative detection of the microbial spoilage of muscle foods: current status and future trends. Food Science & Technology, 12(11):414–424, ? 2001. ga01aDavidEllis. [324] Fengzhan Tian, Yuchang Lu, and Chunyi Shi. Learning Bayesian networks with hidden variables using the combination of EM and evolutionary algorithms. In David Cheung, Graham J. Williams, and Qing Li, editors, Advances in Knowledge Discovery and Data Mining, Proceedings of the 5th Pacific-Asia Conference, PAKDD 2001, volume 2035 of Lecture Notes in Artificial Intelligence, pages 568–574, Hong Kong (China), 16.-18. April 2001. Springer-Verlag, Berlin. ga01aFengzhanTian. [325] Hani Hagras, Victor Callaghan, and Martin Colley. Outdoor mobile robot learning and adaptation. IEEE Robotics & Automation Magazine, 8(3):53–69, September 2001. ga01aHHagras. [326] Hisao Ishibuchi, Tomoharu Nakashima, and M. Nii. Learning of neural networks with GA-based instance seletion. In M. H. Smith, W. A. Gruver, and L. O. Hall, editors, Joint 9th IFSA World Cingress and 20th NAFIPS International Conference, volume 4, pages 2102–2107, Vancouver, BC, Canada, 25.-28.July 2001. IEEE, Piscataway, NJ. * www/IEEE ga01aHIshibuchi. [327] H. Kanoh and A. Hosokawa. Learning of biological behaviour by classifier. In A. Sachenko, editor, International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, volume ?, pages 228–233, Crimea, Ukraine, 1.-4.July 2001. IEEE, Piscataway, NJ. * www/IEEE ga01aHKanoh. [328] Joshua A. Singer. Co-evolving a neural-net evaluation function for Othello by combining genetic algorithms and reinforcement learning. In V. N. Alexandrov, J. J. Dongarra, B. A. Juliano, R. S. Renner, and C. J.Kenneth Tan, editors, Computational Science - ICCS 2001, International Conference, volume LNCS of 2074, pages 377–389, San Francisco, CA, 28.-30. May 2001. Springer-Verlag Berlin Heidelberg. * www /Springer ga01aJASinger. [329] Jes´ us S. Aguilar-Ruiz, Isabel Ramos, José C. Riquelme, and Miguel Toro. An evolutionary approach to estimating software development projects. Information and Software Technology, 43(14):875–882, 15. December 2001. ga01aJSAguilar-Ruiz. [330] K. L. Downing. Adaptive genetic programs via reinforcement learning. In Proceedings of the Genetic and Evolutionary Computation Conference /GECCO-2001), volume ?, page ?, ? 2001. †[372] ga01aKDowning. [331] Luc Pigeon, Jordi Inglada, and Basel Solaiman. Genetic algorithms for multiagent fusion system learning. In Belur V. Dasarathy, editor, Sensor Fusion: Architectures, Algorithms, and Applications V, volume SPIE4385, pages 87–95, ?, March 2001. The International Society for Optical Engineering. * www/SPIE Web ga01aLPigeon. [332] Marco Russo. Distributed fuzzy learning using the MULTISOFT machine. IEEE Transactions on Neural Networks, 12(3):475–484, May 2001. ga01aMRusso. [333] Michael H¨ usken, Christian Igel, and Marc Toussaint. Task-dependent evolution of modularity in neural networks – a quantitative case study. In Erik D. Goodman, editor, 2001 Genetic and Evolutionary Computation Conference (GECCO 2001) – Late-Breaking Papers, pages 187–193, San Francisco, CA, ? 2001. ? ga01aMichaelHusken. [334] Toshio Fukuda and Yasuhisa Hasegawa. Learning and adaptation in robotics. In S. Singh, N. Murshed, and W. Kropatsch, editors, Advances in Pattern Recognition - ICAPR 2001, Second International Conference, volume LNCS of 2013, pages 12–23, Rio de Janeiro (Brazil), 11.-14. March 2001. Springer-Verlag Berlin Heidelberg. * www /Springer ga01aTFukuda. [335] Thomas R. Cundari and Marco Russo. Database mining using soft computing techniques. an integrated neural network-fuzzy logic-genetic algorithm approach. Journal of Chemical Information and Computer Sciences, 41(2):281–287, March/April 2001. ga01aTRCundari. [336] Thomas Ragg. Bayesian learning and evolutionary parameter optimization. In F. Baader, G. Brewka, and T. Eiter, editors, KI 2001, volume 2174 of Lecture Notes in Artificial Intelligence, pages 48–62, Berlin, ? 2001. Springer-Verlag. ga01aThomasRagg. [337] Thomas Riechmann. Genetic algorithm learning and evolutionary games. Journal of Economic Dynamics & Control, 25(?):1019–1037, ? 2001. ga01aThomasRiechmann. [338] Oscar Cord´ on, Francisco Herrera, and Pedro Villar. Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base. IEEE Transactions on Fuzzy Systems, 9(4):667–674, August 2001. ga01bOCordon.

50


[339] Frank Schmiedle, Nicole Drechsler, Daniel Große, and Rolf Drechsler. Heuristic learning based on genetic programming. Genetic Programming and Evolvable Machines, 3(4):363–388, December 2002. ga02aFrankSchmiedle. [340] Ingo Sch¨ oder, Horia F. Pop, Wolfgang Menzel, and Kilian A. Foth. Learning weights for a natural language grammar using genetic algorithms. pages 243–247, 2002. ga02aISchroder. [341] Jong-Cheol Song, Beoung-Xu Moon, Hyun-Soo Chung, So-Hyun Son, and Seong-Yong Lee. System for automatically classifying documents by category learning using a genetic algorithm and a term cluster and method thereof, 2002. (U.S. patent no. 2002/0078044. Issued June 20 2002) ga02aJ-CSong. [342] Jacky Baltes and Yongjoo Park. Comparison of several machine learning techniques in pursuit-evasion games. In A. Birk, S. Coradeschi, and S. Tadokoro, editors, RoboCup 2001: Robot Soccer World Cup V, volume LNAI of 2377, pages 269–274, ?, ? 2002. Springer-Verlag Berlin Heidelberg. * www /Springer ga02aJBaltes. [343] James Smith. On approriate adaption levels for the learning of gene linkage. Genetic Programming and Evolvable Machines, 3(2):129–156, 2002. †toc ga02aJamesSmith. [344] Kim-Hui Yap and Ling Guan. A computational reinforced learning scheme to blind image deconvolution. IEEE Transactions on Evolutionary Computation, 6(1):2–15, February 2002. ga02aK-HYap. [345] Krzysztof Krawiec. Genetic programming-based construction of features for machine learning and knowledge discovery tasks. Genetic Programming and Evolvable Machines, 3(4):329–343, December 2002. ga02aKKrawiec. [346] Nobutada Fujii, Tomoaki Hosoi, Itsuo Hatono, and Kanji Ueda. Reinforcement learning approach to lineless assembling system. pages 121–126, 2002. ga02aNobutadaFujii. [347] R. Abu-Zitar and A. M. A. Nuseirat. Performance evaluation of genetic algorithms and evolutionary programming in optimization and machine learning. Cybernetics and Systems, 33(3):203–223, April-May 2002. †ISI ga02aRAbu-Zitar. [348] U. Nehmzow. Physically embedded genetic algorithm learning in multi-robot scenarios: The PEGA algorithm. In Proceedings of the International Workshop on Epigenetic Robotics and Robotics (EPIROB-2002), page ?, ? 2002. †[936] ga02aUNehmzow. [349] Y. Niv, D. Joel, I. Meilijson, and E. Ruppin. Evolution of reinforcement learning in uncertain environments: a simple explanation for complex foraging behaviors. Adaptive Behavior, 10(1):5–24, ? 2002. †[936] ga02aYNiv. [350] Z. S. H. Chan, H. W. Ngam, A. B. Rad, and T. K. Ho. Alleviating ’overfitting‘ via genetically-regularised neural network. Electronics Letters, 38(15):809–810, 18. July 2002. ga02aZSHChan. [351] Hillol Kargupta and Samiran Ghosh. Toward machine learning through genetic code-like transformations. Genetic Programming and Evolvable Machines, 3(3):231–258, 2002. †toc ga02bHillolKargupta. [352] P. Augustsson, K. Wolff, and Peter Nordin. The evolution of variable learning rates. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), volume ?, pages 52–59, ?, ? 2002. †[936] ga02bPAugustsson. [353] A. Eriksson, G. Capi, and K. Doya. Evolution of meta-parameters in reinforcement learning algorithms. In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS-2003), volume ?, pages 412–417, ?, ? 2003. IEEE, Piscataway, NJ. †[936] ga03aAEriksson. [354] Gregory A. Holifield and Annie S. Wu. A genetic algorithm as learning method based on geometric representations. In ?, editor, Genetic and Evolutionary Computation - GECCO 2003, Proceedings, volume 2724 of Lecture Notes in Computer Science, pages 1588–1589, ?, ? 2003. Springer-Verlag, Heidelberg. ga03aGAHolifield. [355] Jem J. Rowland. Interpreting analytical spectra with evolutionary computation. In Gary B. Fogel and David W. Corne, editors, Evolutionary Computation in Bioinformatics, pages 341–366. Morgan Kaufmann Publishers, New York, 2003. †TKKpaa ga03aJemJRowland. [356] Matthew G. Smith and Larry Bull. Feature construction and selection using genetic programming and a genetic algorithm. In Conor Ryan, Terence Soule, Maarten Keijzer, Edward Tsang, Riccardo Poli, and Ernesto Costa, editors, Genetic programming, 6th European Conference, EuroGP 2003 Proceedings, volume 2610 of Lecture Notes in Computer Science, pages 229–237, Essex (UK), 14.-16. April 2003. Springer-Verlag, Berlin. ga03aMatthewGSmith.

Bibliography

51

[357] Stefan Elfwing, Eiji Uchibe, and Kenji Doya. An evolutionary approach to automatic construction of the structure in hierarchical reinforcement learning. In Proceedings of the Genetic and Evolutionary Computation Conference, volume ?, page ? ?, ? 2003. †ga07aSElfwing = [372] ga03aSElfwing. [358] Shu-Heng Chen and Tzu-Wen Kuo. Overfitting or poor learning: A critique of current financial applications of GP pages =. [359] B. Curry and P. H. Morgan. Evaluating Kohonen’s learning rule: An approach through genetic algorithms. European Journal of Operational Research, 154(1):191–205, 1. April 2004. * www /SceinceDirect ga04aBCurry. [360] David I. Ellis, David Broadhurst, and Royston Goodacre. Rapid and quantitative detection of the microbial spoilage of beef by Fourier transform infrared spectroscopy and machine learning. Analytica Chimica Acta, 514(?):193–201, ? 2004. ga04aDavidEllis. [361] Janne Haverinen. Adaptation Through Stochastic Evolutionary Neuron Migration Process. PhD thesis, University of Oulu, Department of Electrical and Information Engineering, 2004. ga04aHaverinen. [362] Jason W. Horihan and Yung-Hsiang Lu. Improving FSM evolution with progressive fitness functions. In Proceedings of the 14th ACM Great Lakes Symposium on VLSI, pages 123–126, Boston, MA, ? 2004. ACM Press, New York. ga04aJWHorihan. [363] Carlos Andrés Pe˜ na-Reyes. Coevolutionary fuzzy modeling. In Coevolutionary fuzzy modeling, volume 3204 of Lecture Notes in Computer Science. Springer-Verlag, Heidelberg, 2004. †www /ISI ga04bCAPena-Reyes. [364] David I. Ellis, David Broadhurst, Sarah J. Clarke, and Royston Goodacre. Rapid identification of closely related muscle foods by vibrational spectroscopy and machine learning. The Analyst, 130(12):1648–1654, ? 2005. ga05aDavidEllis. [365] Jacek Frankowski. Neural network with hybrid learning strategy for financial time series forecasting. In Yuping Wang, Yiu ming Cheung, and Hailin Liu, editors, Advances in Computational Intelligence and Security, Proceedings of the Workshop of 2005 International Conference on Computational Intelligence and Security, pages 72–76, Xian (China), 15. December 2005. Xidian University Press. ga05aJFrankowski. [366] Louise C. Kenny, Warwick B. Dunn, David I. Ellis, Jenny Myers, Philip N. Baker, and Douglas B. Kell. Novel biomarkers for pre-eclampsia detected using metabolomics and machine learning. Metabolomics, 1(3):227–234, July 2005. ga05aLCKenny. [367] Martin Pelikan. Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms. Springer-Verlag, Berlin, 2005. †Pelikan ga05aMPelikan. [368] Stefan Elfwing, Eiji Uchibe, Kenji Doya, and Henrik I. Christensen. Biologically inspired embodied evolution of survival. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC-2005), volume ?, pages 2210–2216, ?, ? 2005. IEEE, Piscataway, NJ. ga05aSElfwing. [369] S. Kamio Hitoshi Iba. Adaptation technique for integrating genetic programming and reinforcement learning for real robots. IEEE Transactions on Evolutionary Computing, 9(3):318–333, ? 2005. †[936] ga05aSKamio. [370] David I. Ellis, David Broadhurst, J. J. Rowland, and Royston Goodacre. Rapid detection method for microbial spoilage using FT-IR and machine learning. In A. van Amerongen, D. Barug, and M. Lauwaars, editors, Rapid Methods for Food and Feed Quality Determination, page ? Wageningen Academic Publishers, Wageningen (The Netherlands), 2006. (in press) †Ellis homepage ga06aDavidEllis. [371] Ian Cloate and Jacobus van Zyl. Fuzzy rule induction in a set covering framework. IEEE Transaction on Fuzzy Systems, 14(1):93–110, February 2006. ga06aIanCloete. [372] Stefan Elfwing, Eiji Uchibe, Kenji Doya, and Henrik I. Christensen. Evolutionary development of hierarchical learning structures. IEEE Transactions on Evolutionary Computations, 11(2):249–264, ? 2007. ga07aSElfwing. [373] Mateem M. Rizki, Louis A. Tamburino, and Michael A. Zmuda. Applications of learning strategies to pattern recognition. In Steven K. Rogers, editor, Applications of Artificial Neural Networks II, volume SPIE-1469, pages 384–391, ?, August 1991. The International Society for Optical Engineering. * www/SPIE Web ga91aMMRizki. [374] Lei Xu and Stan Klasa. Recent advances on techniques and theories of feedforward networks with supervised learning. In Dennis W. Ruck, editor, Science of Artificial Neural Networks, volume SPIE-1710, pages 317– 328, ?, July 1992. The International Society for Optical Engineering. * www/SPIE Web ga92aLXu.

52


[375] Robert G. Reynolds and William Sverdlik. Learning the behavior of Boolean circuits from examples using cultural algorithms. In Firooz A. Sadjadi, editor, Adaptive and Learning Systems II, volume SPIE-1962, pages 177–188, ?, September 1993. The International Society for Optical Engineering. * www/SPIE Web ga93aRGReynolds. [376] Alan C. Schultz. Learning robot behaviors using genetic algorithms. In ?, editor, Proceedings of the International Symposium on Robotics and Manufacturing, volume ?, page ?, ?, 14.-18. August 1994. ? ga94aACSchultz. [377] Takeshi Agui, T. Kakuyama, Hiroshi Nagahashi, and Tomoharu Nagao. A study on learning of neural networks using a genetic algorithm. J. Inst. Image Electron. Eng. Jpn. (Japan), 23(2):96–101, April 1994. * EEA 96005/94 ga94aAgui. [378] B. Bhanu and S. Lee. Genetic Learning for Adaptive Image Segmentation. Kluwer Academic Publishers, Boston, MA, 1994. †[937] ga94aBBhanu. [379] Shumeet Baluja. Population-based incremental learning. a method for integrating genetic search based function optimization and competitive learning. Report CMU-CS-94-163, Carnegie-Mellon University, Department of Computer Science, 1994. (available via www URL: http://rose.mercury.acs.cmu.edu:80/) ga94aBaluja. [380] Andrea Bonarini. Evolutionary learning of general fuzzy rules with biased evaluation functions: competition and cooperation. In ICEC’94 [909], pages 51–56. ga94aBonarini. [381] James J. Buckley, P. Krishnamraju, Kevin D. Reilly, and Yoichi Hayashi. Genetic learning algorithms for fuzzy neural nets. In FuzzyIEEE94 [912], pages –. ga94aBuckley. [382] Chuen-Tsai Sun and Ming-Da Wu. Multi-stage genetic algorithm learning in game playing. In Proceedings of the First International Joint Conference of the North American Fuzzy Information Processing Society Biannual Conference. The Industrial Fuzzy Control and Intelligent Systems Conference, and The NASA Joint Technology Workshop on Neural Networks and Fuzzy Logic, pages 223–227, San Antonio, TX, 18.21. December 1994. IEEE, New York. †CCA 12115/95 ga94aCTSun. [383] H. G. Cobb and P. Bock. Using a genetic algorithm to search for the representational bias of a collective reinforcement learner. In Davidor et al. [910], page ? †conf. prog. ga94aCobb. [384] J. E. Cooling and B. Korousic-Seljak. Task scheduling – using neural networks within hardware coprocessors. In Proceedings of the 7th Mediterranean Electrotechnical Conference (MELECON94), volume 1, pages 317–320, Antalya (Turkey), 12.-14. April 1994. IEEE, New York. †EI M048014/95 ga94aCooling. [385] David Perry Greene and Stephen F.Smith. Genetic algorithms for machine learning. [389], chapter Competition-based induction of decision models from examples, page ? †www.amazon.com ga94aDPGreene. [386] Chris Gathercole and Peter Ross. Dynamic training subset selection for supervised learning in genetic programming. In Davidor et al. [910], page ? †conf. prog. ga94aGathercole. [387] Attilio Giordana, Lorenza Saitta, and Floriano Zini. Learning disjunctive concepts with distributed genetic algorithms. In ICEC’94 [909], pages 115–119. ga94aGiordana. [388] Pierre Yves Glorennec. Fuzzy Q-learning and evolutionary strategy for adaptive fuzzy control. In EUFIT’94 [911], pages 35–40. ga94aGlorennec. [389] John J. Grefenstette. Genetic Algorithms for Machine Learning. Kluwer Academic Publishers, ?, 1994. †www.amazon.com ga94aGrefenstette. [390] Xiaohua Hu and Nick Cercone. Learning decision rules from relational databases. pages –, 1994. ga94aHu. [391] Shih-Lin Hung and H. Adeli. A parallel genetic/neural network learning algorithm for MIMD shared memory machines. IEEE Transactions on Neural Networks, 5(6):900–909, November 1994. ga94aHung. [392] Yasumasa Ikuno, Hiroaki Kawabata, Yoshiaki Shirao, Masaya Hirata, Toshikuni Nagahara, and Yashio Inagaki. Application of an improved genetic algorithm to the learning of neural networks. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, E77-A(4):731–735, April 1994. ga94aIkuno. [393] Cesary Z. Janikow. Genetic algorithms for machine learning. [389], chapter A knowledge-intensive genetic algorithm for supervised learning, page ? †www.amazon.com ga94aJanikow. [394] Jérˆ ome Kodjabachian and Jean-Arcady Meyer. Development, learning and evolution. In P. Gaussier and J. Nicoud, editors, Proceedings of the PerAc’94. From Perception to Action, pages 96–109, Lausanne (Switzerland), 7.-9. September 1994. IEEE Computer Society Press, New York. * CCA 44593/95 ga94aKodjabachian.

Bibliography

53

[395] Masao Kubo and Yukinori Kakazu. Acquisition of the various coordinated motions of multi-agent system on soccer game. In ICEC’94 [909], pages 686–691. ga94aKubo. [396] Lon-Chan Chu. Algorithms for combinatorial optimization in real-time and their automated refinements by genetics-based learning. PhD thesis, University of Illinois at Urbana-Champaign, 1994. * DAI Vol 55 No 12 ga94aLCChu. [397] Douglas B. Lynch and David C. Kuncicky. A knowledge-based genetic heuristic for learning certainty factors. In ICEC’94 [909], pages 125–129. ga94aLynch. [398] Jorge Muruzabal and A. Munoz. Diffuse pattern learning with fuzzy ARTMAP and PASS. In Davidor et al. [910], page ? †conf. prog. ga94aMuruzabal. [399] Gilberto Nakamiti and Fernando Gomide. An evolutive fuzzy mechanism based on past experiences. In EUFIT’94 [911], pages 1211–1217. ga94aNakamiti. [400] Mircea Gh. Negoita and Eugene Roventa. Knowledge learning in expert systems by genetic algorithms. In EUFIT’94 [911], pages 1121–1125. ga94aNegoita. [401] Chris Nikolopoulos and Shannon Duvendack. A hybrid machine learning system and its application to insurance underwriting. In ICEC’94 [909], pages 692–695. ga94aNikolopoulos. [402] J. Okamoto, Y. Sugimoto, and S. Hosokawa. A learning method for a feed forward type neural network by GA. Transactions of the Institute of Electronics, Information, and Communication Engineers D-II (Japan), J77D-II(2):461–465, February 1994. (in Japanese) †CCA 41809/94 ga94aOkamoto. [403] Mukesh J. Patel and Vittorio Maniezzo. NN’s and GA’s: evolving co-operative behaviour in adaptive learning agents. In ICEC’94 [909], pages 290–295. ga94aPatel. [404] Witold Pedrycz. Garel: a hybrid genetic learning in fuzzy relational equations. In FuzzyIEEE94 [912], pages –. ga94aPetrycz. [405] Mateen M. Rizki, Louis A. Tamburino, and Michael A. Zmuda. E-MORPH: A two-phased learning system for evolving morphological classification systems. In Sebald and Fogel [913], page ? †conf.prog ga94aRizki. [406] Shu-Chung Leung, Andrew Luk, and Sin-Chun Ng. Fast convergent genetic-type search for multi-layered network. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, E77-A(9):1484–1492, September 1994. * EI M057032/95 CCA 507/95 ga94aSHLeung. [407] Robert Smalz and Michael Conrad. Combining evolution with credit apportionment: A new learning algorithm for neural nets. Neural Networks, 7(2):341–351, January 1994. ga94aSmalz. [408] Kirk Twardowski. An associative architecture for genetic algorithm-based machine learning. Computer, 27(11):27–38, November 1994. ga94aTwardowski. [409] Wen Wu and Richard J. Mammone. Relabeling exchange method REM for learning in neural networks. In James L. Flanagan, Richard J. Mammone, Albert E. Brandenstein, Edward R. Pike, Stelios C. Thomopoulos, Marie-Paule Boyer, H. K. Huang, and Osman M. Ratib, editors, Substance Identification Analytics, volume SPIE-2093, pages 254–263, ?, February 1994. The International Society for Optical Engineering. * www/SPIE Web ga94aWWu. [410] P. A. Whigham. Genetic programming and spatial information. In C. Zhang, J. Debenham, and D. Lukose, editors, Proceedings of the Seventh Australian Joint Conference on Artificial Intelligence, pages 124–131, Armidale - NSW (Australia), 21.-25. November 1994. World Scientific Publ. Co., Singapore. †CCA 66670/96 ga94aWhigham. [411] J. Ypsilantis and H. Yee. Machine learning of diagnostic knowledge for a power distribution fault diagnostician using a genetic algorithm. In ?, editor, Proceedings of the 12th Triennial World Congress of the International Federation of Automatic Control, volume 4, pages 809–812, Sydney (Australia), 18.-23. July 1993 1994. Pergamon, Oxford (UK). * CCA 85367/95 ga94aYpsilantis. [412] Alan C. Schultz. Learning robot behaviors using genetic algorithms. In Mohammad Jamshidi and Charles Nguyen, editors, Intelligent Automaton and Soft Computing: Trends in Research, Development, and Applications, Proceedings of the First World Automation Congress, volume 1, pages 607–612, ?, ? 1994. TSI Press, Albuquerque, NM. †GA /George Mason U ga94bACSchultz. [413] C. Adami and C. T. Brown. Evolutionary learning in the 2D artificial life system “Avida”. In ?, editor, Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems, pages 377–381, Cambridge, MA, USA, 6.-8. July 1994. MIT Press, Cambridge, MA. * CCA43696/96 ga94bAdami.

54


[414] Takeshi Furuhashi, Yujiro Miyata, K. Nakaoka, and Yoshiki Uchikawa. A new approach to genetic based machine learning for efficient improvement of local portions of chromosomes. In 1994 IEEE symposium on emerging technologies and factory automation, pages 458–465, Tokyo (Japan), 6.-10. November 1994. IEEE New York. †CCA 44291/95 ga94bFuruhashi. [415] Attilio Giordana, Lorenza Saitta, and Floriano Zini. Learning disjunctive concepts by means of genetic algorithms. In William W. Cohen and Haym Hirsh, editors, Machine Learning, Proceedings of the Eleventh International Conference, pages 96–104, New Brunswick, NJ, 10.-13. July 1994. Morgan Kaufmann, San Mateo, CA. ga94bGiordana. [416] Man Leung Wong and Kwong Sal Leung. Learning first-order relations from noisy databases using genetic algorithms. Asian Computer Weekly, ?(?):119–124, ? 1994. †[938] ga94bMLWong. [417] Mukesh J. Patel and Marco Dorigo. Adaptive learning of a robot arm. In Fogarty [914], pages 180–194. †Fogarty ga94bPatel. [418] N. Saravanan. Reinforcement learning using evolutionary programming. In Sebald and Fogel [913], page ? †conf.prog ga94bSaravanan. [419] Marc Schoenauer and Michéle Sebag. Controlling crossover through inductive learning. In Davidor et al. [910], page ? †conf. prog. ga94bSchoenauer. [420] Toshiyuki Masui. Evolutionary learning of graph layout constraints from examples. In Proceedings of the Seventh Annual Symposium on User Interface Software and Technology, pages 103–108, Marina del Rey, CA, USA, 2.-4. November 1994. ACM, New York, NY. * CCA58146/96 ga94bToshiyukiMasui. [421] T. Unemi, M. Nagayoshi, N. Hirayama, T. Nade, K. Yano, and Y. Masujima. Evolutionary differentiation of learning abilities a case study on optimizing parameter values in Q-learning by a genetic algorithm. In ?, editor, Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems, pages 331–336, Cambridge, UK, 6.-8. July 1994. MIT Press, Cambridge, MA. * CCA43999/96 ga94bUnemi. [422] Kenneth A. De Jong, William M. Spears, and Diana F. Gordon. Genetic algorithms for machine learning. [389], chapter Using genetic algorithms for concept learning, page ? †www.amazon.com ga94cDeJong. [423] Marco Dorigo and Marco Colombetti. Robot shaping: Developing autonomous agents through learning. Artificial Intelligence, 71(2):321–370, December 1994. ga94cDorigo. [424] Terence C. Fogarty, N. S. Ireson, and Lawrence Bull. Genetics based machine learning – applications in industry and commerce. In Vic J. Rayward-Smith, editor, Applications of Modern Heuristic Techniques, page ? Alfred Waller, Ltd., Henley-on-Thames (UK), 1994. (to appear) †Fogarty ga94cFogarty. [425] Takeshi Furuhashi, N. Nakaoka, and Y. Uchikawa. A new approach to genetic based machine learning and an efficient finding of fuzzy rules. In Proceedings of the Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms, pages 114–122, Nagoya (Japan), 9.-10. August 1994 1994. Springer-Verlag, Berlin (Germany). ga94cFuruhashi. [426] Man Leung Wong and Kwong Sal Leung. Learning first-order relations from noisy databases using genetic algorithms. In ?, editor, Proceedings of the 2nd Singapore International Conference on Intelligent Systems (SPICIS’94), pages B159–B164, Singapore, 14.-17. November 1994. Japan-Singapore AI Centre, Singapore. †CCA 66563/96 ga94cMLWong. [427] N. Saravanan. Learning of strategy parameters in evolutionary programming: An empirical study. In Sebald and Fogel [913], page ? †conf.prog ga94cSaravanan. [428] Gilles Venturini. Improving genetic algorithms for concept learning. In ICEC’94 [909], pages 634–638. ga94cVenturini. [429] Darrell Whitley, Stephen Dominic, Rajarshi Das, and Charles W. Anderson. Genetic algorithms for machine learning. [389], chapter Genetic reinforcement learning for neurocontrol problems, page ? †www.amazon.com ga94cWhitley. [430] Gilles Venturini. Analyzing French justice with a genetic-based inductive algorithm. Applied Artificial Intelligence, 8(4):565–577, October-December 1994. * CCA 17427/95 ga94dVenturini. [431] Terence C. Fogarty and N. S. Ireson. Bayesian classifiers for credit control – a comparison with other machine learning methods. IMA Journal of Mathematics Applied in Business and Industry, 5(?):63–75, ? 1994. †Fogarty ga94fFogarty. [432] Terence C. Fogarty, N. S. Ireson, and L. Bull. Genetics based machine learning-applications in industry and commerce. In ?, editor, Proceedings of the Adaptive Computing and Information Processing, volume 2, pages 509–530, London, UK, 25.-27. January 1994. Unicom Seminars, Uxbridge, UK. * CCA31283/96 ga94hFogarty.

Bibliography

55

[433] John R. Koza. Advances in genetic programming. In Kenneth E. Kinnear, Jr., editor, Advances in Genetic Programming, chapter 5. Scalable learning in genetic programming using automatic function definition, pages 99–117. MIT Press, Cambridge, MA, 1994. †cessu ga94iKoza. [434] A. Ugur and Michael Conrad. Multiple level evolutionary learning in neuronal pattern recognition. In ?, editor, Proceedings of the Fourth Annual Conference on Evolutionary Programming, pages 271–288, San Diego, CA, 1.-3. March 1995. MIT Press, Cambridge, MA. †CCA34467/97 ga95aAUgur. [435] M. I. A. Abdalla. Genetic algorithm for learning neural networks. Egypt. Comput. J. (Egypt), 23(2):132–148, 1995. †CCA 626/97 ga95aAbdalla. [436] Jasmina Arifovic and C. Eaton. Coordination via genetic learning. Comput. Econ., 8(3):181–203, 1995. * CCA 77500/95 ga95aArifovic. [437] S. Augier, G. Venturini, and Y. Kodratoff. Learning first order logic rules with genetic algorithm. In ?, editor, Evolution Artificielle 95 (EA’95), page ?, Brest (France), 4.-6. September 1995. Springer-Verlag, Berlin. †conf.prog ga95aAugier. [438] Byoung-Tak Zhang, P. Ohm, and Heinz M¨ uhlenbein. Learning to predict by evolutionary neural trees. In ?, editor, Proceedings of the 1995 International Neural Network Society Annual Meeting, volume 1, pages 823–826, Washington, DC (USA), 17.-21. July 1995. Lawrence Erlbaum Associates, Mahwah, NJ (USA). †CCA49060/97 ga95aByoZhang. [439] Chuen-Tsai Sun and Ming-Da Wu. Self-adaptive genetic algorithm learning in game playing. In ICEC’95 [919], pages 814–818. †prog. ga95aCTSun. [440] Helen G. Cobb. Is the genetic algorithm a cooperative learner? NASA Contract Report AD-A294112, Navy Center for Applied Research in Artificial Intelligence, 1995. * N95-34499 ga95aCobb. [441] Clare Bates Congdon. A comparison of genetic algorithms and other machine learning systems on a complex classification task from common disease research. PhD thesis, The University of Michigan, 1995. * DAI Vol 56 No 4 ga95aCongdon. [442] Ulrich Dorndorf and Erwin Pesch. Evolution based learning in a job shop scheduling environment. Computers & Operations Research, 22(1):25–40, 1995. ga95aDorndorf. [443] Gyoung H. Kim and C. S. George Lee. Genetic reinforcement learning approach to the machine scheduling problem. In Proceedings of the 1995 IEEE International Conference on Robotics and Automation, volume 1, pages 196–201, Nagoya (Japan), 21.-27. May 1995. IEEE, New York. ga95aGHKim. [444] Antonella Giani, Fabrizio Baiardi, and Antonina Starita. Q-learning and parallelism in evolutionary rule based systems. In Pearson et al. [916], pages 376–379. ga95aGiani. [445] Kuo-Tsai Lee, Kaung-Tsang Jean, and Yung-Yaw Chen. Genetic-based reinforcement learning of fuzzy logic control systems. In IEEE-SMC’95 [939], pages 1057–1060. * EI M035724/96 ga95aK-TLee. [446] Sejal Kamani. Behavior learning and individual cooperation in autonomous agents as a result of interaction dynamics with the environment. In John R. Koza, editor, Genetic Algorithms at Stanford 1995, page ?, Stanford, CA, 1995. Stanford Bookstore. †Koza ga95aKamani. [447] Rhee M. Kil. Learning with genetic algorithm. In Korea-Australia EC’95 [940], pages 43–50. ga95aKil. [448] Leslie Knight and Sandip Sen. PLEASE: A prototype learning system using genetic algorithms. In Eshelman [915], page ? †prog ga95aKnight. [449] Vladim´ır Kvasniˇcka, Martin Pelik´ an, and Jiˇr´ı Posp´ıchal. Hill-climbing with learning: An abstraction of genetic algorithms. Technical report, Slovak Technical University, Bratislava, 1995. †[941] ga95aKvasnicka. [450] Luis Magdalena, Juan R. Velasco, G. Fern´ andez, and F. Monasterio. Evolutionary-based learning applied to fuzzy controllers. In Proceedings of the 4th IEEE International Conference on Fuzzy Systems and the 2nd International Fuzzy Engineering Symposium, volume III, pages 1111–1118, ?, March 1994. IEEE, New York. †[942] ga95aMagdalena. [451] Kaoru Nakano, Hideaki Hiraki, and Shiro Ikeda. A learning machine that evolves. In ICEC’95 [919], pages 808–813. †prog. ga95aNakano. [452] Witold Pedrycz. Genetic algorithms for learning in fuzzy relational structures. Fuzzy Sets and Systems, 69(1):37–52, January 1995. * EI M079072/95 ga95aPedrycz. [453] Mitchell A. Potter, Kenneth A. De Jong, and John J. Grefenstette. A coevolutionary approach to learning sequential decision rules. In Eshelman [915], page ? †prog ga95aPotter.

56


[454] S. Chen, Y. Wu, and K. Alkadhimi. A two-layer learning method for radial basis function networks using combined genetic and regularised OLS algorithms. In IEE/IEEE Sheffield ’95 [918], pages 245–249. †conf.prog ga95aSChen. [455] John W. Sheppard and Steven L. Salzberg. Combining genetic algorithms with memory based reasoning. In Eshelman [915], page ? †prog ga95aSheppard. [456] Koji Shimojima, Toshio Fukuda, and Yasuhisa Hasegawa. RBF-fuzzy system with GA based unsupervised/supervised learning method. In Proceedings of the 4th IEEE International Conference on Fuzzy Systems and the 2nd International Fuzzy Engineering Symposium, volume 1, pages 253–258, Yokohama (Japan), 20.-24. March 1995. IEEE, New York. ga95aShimojima. [457] Russel James Stonier. Adaptive learning using genetic algorithms and evolutionary programming in robotic systems. In Korea-Australia EC’95 [940], pages 183–198. ga95aStonier. [458] Louis A. Tamburino, Michael A. Zmuda, and Mateen M. Rizki. Generating pattern-recognition systems using evolutionary learning. IEEE Expert, 10(3):63–68, June 1995. ga95aTamburino. [459] Gilles Venturini. Apprentissage et algorithmes genetiques [genetic algorithms in machine learning]. Revue d’Intelligence Artificielle, ?(?):?, ? 1995. (in French; to appear in) †Venturini ga95aVenturini. [460] Wu Meng and Feng Guangzeng. Multisolution learning algorithm for fuzzy rules. In Proceedings of the 1995 IEEE International Symposium on Circuits and Systems-ISCAS 95, volume 1, pages 478–481, Seattle, WA, April 30.- May 3. 1995. IEEE, Piscataway, NJ. †EI M197403/95 ga95aWMeng. [461] Weixin Xie, Wenhua Li, and Xinbo Gao. Fuzzy-Kohonen-clustering neural network trained by genetic algorithm and fuzzy competition learning. In Shuzi Yang, Ji Zhou, and Cheng-Gang Li, editors, International Conference on Intelligent Manufacturing, volume SPIE-2620, pages 493–498, ?, August 1995. The International Society for Optical Engineering. * www/SPIE Web ga95aWXie. [462] Raul Sidnei Wazlawick and Antonio Carlos da Rocha Costa. Non-supervised sensory-motor agents learning. In Pearson et al. [916], pages 49–52. ga95aWazlawick. [463] P. R. Weller, R. Summers, and A. C. Thompson. Using a genetic algorithm to evolve an optimum input set for a predictive neural network. In IEE/IEEE Sheffield ’95 [918], pages 256–258. †conf.prog ga95aWeller. [464] Zhengjun Pan, Lishan Kang, Jun He, and Yong Liu. An evolutionary approach to adaptive model-building. In Yao [943], pages 236–244. * CCA 77660/95 ga95aZPan. [465] S. Endo and A. Ohuchi. Genetic based concept learning from positive examples. In Proceedings of the 34th SICE Annual Conference, pages 1619–1622, Hokkaido, Japan, 26.-28. July 1995. Soc. Instrum & Control Eng. 1995, Tokyo, Japan. †EEA87129/95 ga95bEndo. [466] Takeshi Furuhashi, K. Nakaoka, and Yoshiki Uchikawa. An efficient finding of fuzzy rules using a new approach to genetic based machine learning. In Proceedings of 1995 International Conference on Fuzzy Systems, volume 2, pages 715–722, Yokohama (Japan), 20.-24. March 1995. IEEE, New York, NY. †CCA60583/95 ga95bFuruhashi. [467] Meng Gan, Hongxiang Lan, and Liming Zhang. A genetic-based method of generating fuzzy rules and membership functions by learning from examples. In Proceedings of the International Conference on Neural Information Processing, volume 1, pages 335–338, Beijing, China, 30. October-2. November 1995. Publishing House of Electron, Beijing, China. †CCA26695/95 ga95bGan. [468] Diana F. Gordon and John J. Grefenstette. Explanations of empirically derived reactive plans. NASA Contract Report AD-A294114, Navy Center for Applied Research in Artificial Intelligence, 1995. * N9534501 ga95bGordon. [469] Tomonori Hashiyama, S. Behrendt, Takeshi Furuhashi, and Yoshiki Uchikawa. Fuzzy controllers for semiactive suspension system generated through genetic algorithms. In IEEE-SMC’95 [939], pages 4361–4366. * EI M035744/96 ga95bHashiyama. [470] Y. Ikkai, M. Inoue, T. Ohkawa, and N. Komoda. A learning method of scheduling knowledge by genetic algorithm. In Proceedings of the 1995 IEEE Symposium on Emerging Technologies and Factory Automation, volume 1, pages 641–648, Paris, France, 10.-13. October 1995. IEEE Computer Society Press, Los Alamitos, CA. †CCA27380/96 ga95bIkkai. [471] Jerzy W. Bala, J. Huang, Haleh Vafaie, Kenneth A. De Jong, and Harry Wechsler. Hybrid learning using genetic algorithms and decision trees for pattern-classification. In C. S. Mellish, editor, Proceedings of the Fourteeth International Joint Conference on Artificial Intelligence (IJCAI-95), volume ?, pages 719–724, Montreal, Que (Canada), 20.-25. August 1995. Morgan Kaufmann Pub. Inc., San Mateo. ga95bJWBala.

Bibliography

57

[472] Y. Kim and C. Eick. Multi-rule-set decision making schemes for a genetic algorithm learning environment for classification tasks. In J. R. McDonnell, R. G. Reynolds, and David B. Fogel, editors, Proceedings of the Fourth Annual Conference on Evolutionary Programming (EP95), page ?, San Diego, CA, 1.-3. March 1995. MIT Press. †conf.prog ga95bKim. [473] Lei Zhang, Lisong Wang, Yuwen Zang, H. Seki, and H. Itoh. On rule checking and learning in an ancupuncture diagnosis fuzzy expert system by genetic algorithm. In Proceedings of the International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium, volume 2, pages 455–460, Yokohama (Japan), 20.-24. March 1995. IEEE, New York, NY. †CCA66657/95 ga95bLZhang. [474] Pat Langley and Herbert A. Simon. Applications of machine learning and rule induction. Communications of the ACM, 38(11):54–64, 1995. ga95bLangley. [475] Luis Magdalena and F. Monasterio. Evolutionary-based learning applied to fuzzy controllers. In Proceedings of the 1995 IEEE International Conference on Fuzzy Systems, volume 3, pages 1111–1118, Yokohama (Japan), 20.-24. March 1995. IEEE, New York, NY. †CCA70161/95 ga95bMagdalena. [476] I. Murase, S. Kaneko, and T. Honda. Inductive learning of primitive shapes by genetic algorithm. In ?, editor, Proceedings of the Second Asian Conference on Computer Vision, volume 2, pages 593–597, Singapore, 5.-8. December 1995. Nanyang Technol. University, Singapore. †EEA86862/96 ga95bMurase. [477] Filippo Neri and Attilio Giordana. A parallel genetic algorithm for concept learning. In Eshelman [915], page ? †prog ga95bNeri. [478] Jan Paredis. Learning at the crossroads of biology and computation. In Proceedings of the International IEEE Symposium on intelligence in Neural and Biological Systems, pages 56–63, Hemdon, VA, 29.-31. May 1995. IEEE. †EI M146836/95 ga95bParedis. [479] Qiangfu Zhao and T. Higuchi. Individual evolutionary algorithm and its application to learning of nearest neighbor based MLP. In ?, editor, Proceedings of the International Workshop on Artificial Neural Networks, pages 396–403, Malaga-Torremolinos, Spain, 7.-9. June 1995. Springer-Verlag, Berlin (Germany). †CCA34616/96 ga95bQZhao. [480] Thorsten Schnier and John S. Gero. Learning representations for evolutionary computation. In Proceedings of the Eighth Australian Joint Conference on Artificial Intelligence, pages 387–394, Canberra, ACT, Australia, 13.-17. November 1995. World Scientific, Singapore. †CCA9297/97 ga95bSchnier. [481] N. Ono, T. Ohira, and A. T. Rahmani. Evolution of intraspecies communication in Q-learning artificial organisms. In Proceedings of the ISCA International Conference, Fourth Golden West Conference on Intelligent Systems, pages 102–105, San Francisco, CA, 12.-14. June 1995. Int. Soc. Comput. & Their Appl.-ISCA, Raleigh, NC. †CCA77972/95 ga95bTOno. [482] Takao Terano, Yoko Ishino, and Kazuyuki Yoshinaga. Integrating machine learning and simulated breeding techniques to analyze the characteristics of consumer goods. In J¨ org Biethahn and Volker Nissen, editors, Evolutionary Algorithms in Management Application, pages 211–224. Springer-Verlag, Berlin, 1995. †Schwehm ga95bTerano. [483] Wen-Ming Li, Chun-Ying Ho, Nan-Ning Zheng, and S. Mori. An evolutionary algorithm for DTCNN template learning. In ?, editor, Proceedings of International Conference on Neural Information Processing, pages 251–254, Beijing (China), 30. October- 2. November 1995. Publishing House of Electron. Ind., Beijing, China. †CCA26580/96 ga95bW-MLi. [484] B. W. Wah, A. Ieumwananonthachai, Lon-Chan Chu, and A. N. Aizawa. Genetics-based learning of new heuristics: rational scheduling of experiments and generalization. IEEE Transactions of Knowlledge and Data Engineering, 7(5):763–785, 1995. †CCA94535/95 ga95bWah. [485] P. A. Whigham and R. I. McKay. Genetic approaches to learning recursive relations. In Yao [943], pages 17–27. †Yao /conf. prog. ga95bWhigham. [486] X. Yang, T. Furuhashi, K. Obata, and Y. Uchikawa. A study on signature verification using a new approach to genetic based machine learning. In Proceedings of the 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century, volume 5, pages 4383–4386, Vancouver, BC (Canada), 22.-25. October 1995. IEEE, New York, NY. †CCA10101/96 ga95bXYang. [487] Weixin Xie, Wenhua Li, and Xinbo Gao. Fuzzy-Kohonen-clustering neural network trained by genetic algorithm and fuzzy competition learning. In Proceedings of the International Conference on Intelligent Manufacturing, volume 2620, pages 493–498, Wuhan, China, 10. June 1995. SPIE - Society of Photo-Optical Instrumentation Engineering, Bellingham, WA (USA). †EI M066538/96 ga95bXie.

58


[488] Shumeet Baluja and Rich Caruana. Removing the genetics from the standard genetic algorithm. Report CMU-CS-95-151, Carnegie-Mellon University, Department of Computer Science, 1995. (also as [495], available via www URL: http://rose.mercury.acs.cmu.edu:80/) ga95cBaluja. [489] Brian Carse and Terence C. Fogarty. Evolutionary learning of temporal behaviour using discrete and fuzzy classifer systems. In Proceedings of the 1995 IEEE International Symposium on Intelligent Control, pages 183–188, Monterey, CA, 27.-29. August 1995. IEEE, New York , NY. †CCA87495/95 ga95cCarse. [490] David E. Moriarty and Risto Miikkulainen. Learning sequential decision tasks. Technical Report AI-95-229, The University of Texas at Austin, Department of Computer Sciences, 1995. * UTCS lop ga95cDEMoriarty. [491] S. Endo and A. Ohuchi. Disjunctive form concept learning system based on genetic algorithm. In Proceedings of the 1995 IEEE International Conference on Systems, man and Cybernetics, volume 5, pages 4696–4701, Vancouver, BC (Canada), 22.-25. October 1995. IEEE, Piscataway, NJ. †EI M038071/95 ga95cEndo. [492] Jan Paredis. Coevolutionary computation. Artificial Life, 2(4):355–375, Summer 1995. ga95cParedis. [493] S. Sen and L. Knight. A genetic prototype learner. In ?, editor, Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, volume 1, pages 725–731, Montreal, Que (Canada), 20.-25. August 1995. Morgan Kaufmann Publishers, San Mateo, CA (USA). †CCA61645/97 ga95cSSen. [494] William M. Spears and Diana F. Gordon. Adaptive strategy selection for concept learning. NASA Contract Report AD-A294086, Naval Research Laboratory, 1995. * N95-34473 ga95cSpears. [495] Shumeet Baluja and Rich Caruana. Removing the genetics from the standard genetic algorithm. In ?, editor, Proceedings of the Twefth International Conference on Machine Learning, volume ?, page ?, Lake Tahoe, CA, July 1995. ? (also as [488], available via www URL: http://rose.mercury.acs.cmu.edu:80/) ga95dBaluja. [496] Peter Nordin and Wolfgang Banzhaf. A genetic programming system learning obstacle avoiding behavior and controlling a miniature robot in real time. Technical Report SysReport 4/95, University of Dortmund, Fachbereich Informatik, 1995. ga95dNordin. [497] Rolf Drechsler and Bernd Becker. Learning heuristics by genetic algorithms. In ?, editor, Proceedings of the ASP-DAC95/CHDL95/VLSI95. Asian and South Pacific Design Automation Conference. IFIP International Conference on Computer Hardware description Languages and their Applications. IFIP International Conference on Very Large Scale Integration, pages 349–352, Chiba (Japan), 29. August- 1. September 1995. Nihon Gakkai Jimu Senta, Tokyo, Japan. †CCA41956/96 ga95eDrechsler. [498] Man Leung Wong and Kwong Sak Leung. An induction system that learns programs in different programming and logic grammars. In Proceedings of the Seventh International Conference on Tools with Artificial Intelligence, volume ?, pages 380–387, Herndon, VA, 5.-8. November 1995. IEEE Computer Society Press, Los Alamitos , CA. †CCA13647/96 ga95eMLWong. [499] Peter Nordin and Wolfgang Banzhaf. Real time evolution of behavior and a world model for a miniature robot using genetic programming. Technical Report SysReport 5/95, University of Dortmund, Fachbereich Informatik, 1995. ga95eNordin. [500] Francisco Herrera, Manuel Lozano, and Jose Luis Verdegay. A learning process for fuzzy control rules using genetic algorithms. Technical Report DECSAI-95108, University of Granada, Department of Computer Science and Artificial Intelligence, 1995. (available via anonymous ftp site decsai.ugr.es directory pub/tech rep/ga-fl file LP-FCR-GA.ps.Z) ga95fHerrera. [501] Francisco Herrera, Manuel Lozano, and Jose Luis Verdegay. Generating fuzzy rules from examples using genetic algorithms. In B. Bouchon-Meunier, R. R. Yager, and Lotfi A. Zadeh, editors, Fuzzy Logic and Soft Computing, pages 11–20. World Scientific, Singapore, 1995. ga95iHerrera. [502] A. Birk. Learning geometric concepts with an evolutionary algorithm. In ?, editor, Proceedings of the Fifth Annual Conference on Evolutionary Programming, pages 83–90, San Diego, CA (USA), 29. February3. March 1996. MIT Press, Cambridge, MA. †CCA99385/97 ga96aABirk. [503] Alan C. Schultz, John J. Grefenstette, and William Adams. RoboShepherd: Learning a complex behavior. In ?, editor, Proceedings of the FLAIRS’96, volume ?, page ?, ?, ? 1996. ? ga96aACSchultz. [504] A. Kamiya, H. Kimura, M. Yamamura, and S. Kobayashi. Power-plant start-up scheduling - a reinforcement learning approach combined with evolutionary computation. Journal of Intelligent & Fuzzy Systems, 6(1):99–116, 1996. †P81724 ga96aAKamiya. [505] Anders Aspn¨ as, Victor Cockcroft, and Jukka Hekanaho. Use of genetic algorithms to learn ligand recognition concepts: Application to the GPCR superfamily. In Jarmo T. Alander, editor, Proceedings of the Second

Bibliography

59

Nordic Workshop on Genetic Algorithms and their Applications (2NWGA), Proceedings of the University of Vaasa, Nro. 11, pages 131–150, Vaasa (Finland), 19.-23. August 1996. University of Vaasa. (available via anonymous ftp site ftp.uwasa.fi directory cs/2NWGA file Aspnas.ps.Z) ga96aAspnas. [506] B. John Oommen and Edward V. de St. Croix. Graph partitioning using learning automata. IEEE Transactions on Computers, 45(2):195–208, February 1996. ga96aBJOommen. [507] Benjamin W. Wah, Arthur Ieumwananonthachai, and Yong-Cheng Li. chapter 5. Generalization of heuristics learned in genetics-based learning, page ? 1996. †toc ga96aBWWah. [508] Gerriet Backer. Learning with missing data using genetic programming. In Proceedings of the First Online Workshop on Soft Computing (WSC1), pages 279–283, WWW (World Wide Web), 19.-30. August 1996. Nagoya University. ga96aBacker. [509] Dario Bianchi. Learning grammatical rules from examples using a credit assignment algorithm. In Proceedings of the First Online Workshop on Soft Computing (WSC1), pages 113–118, WWW (World Wide Web), 19.-30. August 1996. Nagoya University. ga96aBianchi. [510] P. Chongistitvatana and J. Polvichai. Learning a visual task by genetic programming. In Proceedings of the 1996 IEEE/RSJ International Conferece on Intelligent Robots and Systems, volume 2, pages 534–540, Osaka, Japan, 4.-8. November 1996. IEEE, New York, NY. †CCA28742/97 ga96aChongist. [511] Oscar Cord´ on, Francisco Herrera, and Manuel Lozano. A three-stage method for designing genetic fuzzy systems by learning from examples. In Voigt et al. [921], pages 720–729. (available via anonymous ftp site decsai.ugr.es directory pub/arai/tech rep/ga-fl file paper 104.ps.Z) ga96aCordon. [512] H. Echizen-Ya, K. Araki, Y. Momouchi, and K. Tochinai. Application of genetic algorithms for examplebased machine translation method using inductive learning and its effectiveness. Transactions of the Information Processing Society of Japan, 37(8):1565–1579, 1996. (In Japanese) †CCA8541/97 ga96aEchizen-Ya. [513] C. F. Eick, Y. J. Kim, N. Secomandi, and E. Toto. Delvaux - an environment that learns Bayesian rulesets with genetic algorithms. In J. K. Lee, J. Liebowitz, and Y. M. Chae, editors, Critical Technology: Proceedings of the Third World Congress on Expert Systems, volume 1, pages 758–765, Seoul (South Korea), 5.-9. February 1996. Cognizant Communication Corp., Elmsford. †P69301 ga96aEick. [514] G. Mayley. The evolutionary cost of learning. In Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pages 458–467, North Falmouth, MA (USA), 9.-13. September 1996. MIT Press, Cambridge, MA. †CCA43124/98 ga96aGMayley. [515] Georges R. Harik and David E. Goldberg. Learning linkage. In Richard K. Belew and Michael D. Vose, editors, Proceedings of the fourth Foundations of Genetic Algorithms Workshop, pages 247–262, Alcala Park, San Diego, 2.-5. August 1996. Morgan Kauffman, San Mateo, CA. † ga96aGRHarik. [516] Elvis Galić and Markus H¨ ohfeld. Improving the generalization performance of multi-layer-perceptrons with population-based incremental learning. In Voigt et al. [921], pages 740–750. ga96aGalic. [517] Attilio Giordana and Filippo Neri. Genetic algorithms in machine learning. AI Communications, 9(1):21–26, 1996. †EI M093968/96 ga96aGiordana. [518] J. Goddard, R. U. Parrazales, I. Lopez, and A. D. de P. Luca. Rule learning in fuzzy systems using evolutionary programs. In Proceedings of the 39th Midwest Symposium on Circuits and Systems, volume 2, pages 703–706, Ames, IA (USA), 18.-21. August 1996. IEEE, New York, NY. †CCA63066/97 ga96aGoddard. [519] Christian Goerick and Tobias Rodemann. Evolution strategies: an alternative to gradient-based learning. In Proceedings of the International Conference on Engineering Applications of Neural Networks, volume 1, pages 479–482, London, UK, 17.-19. June 1996. Syst. Eng. Assoc., Turku (Finland). ga96aGoerick. [520] John J. Grefenstette. Genetic learning for adaptation in autonomous robots. In Proceedings of the Sixth International Symposium on Robotics and Manufacturing, pages 265–270, Montpellier (France), 28.-30. May 1996. ASME, New York, NY (USA). †CCA96867/99 ga96aGrefenstette. [521] H. Ito and Tatsumi Furuya. Memory-based neural network and its application to a mobile robot with evolutionary and experience learning. In ?, editor, Proceedings of the First International Conference Evolvable Systems: From Biology to Hardware, pages 234–246, Tsukuba, Japan, 7.-8. October 1996. Springer-Verlag, Berlin (Germany). †CCA70638/97 ga96aHIto. [522] Hugues Juillé and Jordan B. Pollack. Dynamics of co-evolutionary learning. In Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pages 526–534, North Falmouth, MA, 9.13. September 1996. MIT Press, Cambridge, MA. †CCA48061/98 ga96aHJuille.

60


[523] Seung-Soo Han and G. S. May. Optimization of neural network structure and learning parameters using genetic algorithms. In Proceedings of the Eighth IEEE International Conference on Tools with Artificial Intelligence, pages 200–206, Toulouse, France, 16.-19. November 1996. IEEE Computer Society Press, Los Alamitos, CA. †CCA8295/97 ga96aHan. [524] J. V. Hansen and R. D. Meservy. Learning experiments with genetic optimization of a generalized regression neural network. Decis Support Syst (Netherlands), 18(3-4):317–325, 1996. †CCA94910/96 ga96aHansen. [525] Jukka Hekanaho. Background knowledge in GA-based concept learning. TUCS Technical Reports 2, University of Turku, Turku Centre for Computer Science, 1996. (also as [225]) ga96aHekanaho. [526] Houfeng Wang and Dawei Dai. An inductive method with genetic algorithm for learning phrase-structurerule of natural language. Wuhan Univ. J. Nat. Sci. (China), 1(3-4):640–644, 1996. †CCA28653/98 ga96aHoufengWang. [527] I. Kuscu. Evolution of learning rules for hard learning problems. In ?, editor, Proceedings of the Fifth Annual Conference on Evolutionary Programming, pages 91–99, San Diego, CA (USA), 29. February- 3. March 1996. MIT Press, Cambridge, MA. †CCA98955/97 ga96aIKuscu. [528] T. Ichimura, T. Takano, and E. Tazaki. Learning of neural networks using hybrid genetic algorithm. In Proceedings of the Fourth European Congress on Intelligent Techniques and Soft Computing, volume 1, pages 526–530, Aachen (Germany), 2.-5. September 1996. Verlag Mainz, Aachen (Germany). †CCA43033/98 ga96aIchimura. [529] Y. Ikkai, M. Inoue, T. Ohkawa, and N. Komoda. A learning method of scheduling knowledge by genetic algorithm. Transactions of the Institute of Electrical Engineera of Japan C, 116-C(5):577–583, 1996. (In Japanese) †CCA15442/97 ga96aIkkai. [530] John S. Gero and Vladimir A. Kazakov. Learning and reusing information in-space layout planning problems using genetic-engineering. Artificial Intelligence in Engineering (UK), 11(3):329–334, 1996. †P75497 ga96aJSGero. [531] Jerzy W. Bala, Kenneth A. De Jong, J. Huang, Haleh Vafaie, and Harry Wechsler. Using learning to facilitate the evolution of features for recognizing visual concepts. Evolutionary Computation, 4(3):297–311, 1996. †BAb129175 ga96aJWBala. [532] J. Zizka. Learning control rules for Takagi-Sugeno fuzzy controllers using genetic algorithms. In Proceedings of the Fourth European Congress on Intelligent Techniques and Soft Computing, volume 2, pages 960–964, Aachen (Germany), 2.-5. September 1996. Verlag Mainz, Aachen (Germany). †CCA44326/98 ga96aJZizka. [533] Handl´ıˇr Jiˇr´ı. The use of evolution program for learning of artificial intelligence systems. In Pavel Oˇsmera, editor, Proceedings of the MENDEL’96, pages 43–46, Brno (Czech Republic), June 1996. Technical University of Brno. ga96aJiri. [534] Ibrahim Kuscu. Evolutionary and incremental methods to solve hard learning problems. In Koza et al. [923], page ? †conf.prog ga96aKuscu. [535] Louis A. Tamburino and Mateen M. Rizki. Resource allocation for a hybrid evolutionary learning system used for pattern recognition. In ?, editor, Proceedings of the Fifth Annual Conference on Evolutionary Programming, pages 207–216, San Diego, CA (USA), 29. February- 3. March 1996. MIT Press, Cambridge, MA. †CCA99386/97 ga96aLATamburino. [536] Leechter Yao. Nonparametric learning of decision regions via the genetic algorithm. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 26(2):313–321, April 1996. ga96aLYao. [537] Zhang Liangjie, Li Yanda, and Chen Huimin. A new global learning algorithm for fuzzy neural networks based on modified Quasi-Newton method and genetic searching techniques. Acta Electronica Sinica (China), 24(11):5–11, 1996. (In Chinese) †CCA9478/97 ga96aLiangjie. [538] A. Likas, K. Blekas, and A. Stafylopatis. Parallel recombinative reinforcement learning: a genetic approach. J. Intell. Syst. (UK), 6(2):145–169, 1996. †CCA77700/96 ga96aLikas. [539] Liu Juan and Li. Weihua. A hybrid genetic algorithm for supervised inductive learning. Wuhan Univ. J. Nat. Sci. (China), 1(3-4):611–616, 1996. †CCA25784/98 ga96aLiuJuan. [540] Long Zhao and Zemin Liu. A genetic algorithm for reinforcement learning. In Proceedings of the 1996 IEEE International Conference on Neural Networks, volume 2, pages 1056–1060, Washington, DC, 3.-6. June 1996. IEEE, New York, NY. †CCA86890/96 ga96aLongZhao. [541] Robert M. Losee. Learning syntactic rules and tags with genetic algorithms for information retrieval and filtering: an empirical basis for grammatical rules. Information Processing & Management, 32(2):185–197, 1996. ga96aLosee.

Bibliography

61

[542] Magnus Rattray and Jonathan L. Shapiro. The dynamics of a genetic algorithm for a simple learning problem. Journal of Physics A: Mathematical and General, 29(23):7451–7473, December 1996. †NASA ADS ga96aMRattray. [543] Min Woong Hwang and Jin Young Choi. Evolutionary learning algorithm for projection neural networks. In ?, editor, Proceedings of the First Asia-Pacific Conference Simulated Evolution and Learning, pages 136–145, Taejon (Korea), 9.-12. November 1996. Springer-Verlag, Berlin (Germany). †CCA78977/97 ga96aMinHwang. [544] Masaharu Munetomo, Yoshiaki Takai, and Yoshiharu Sato. Genetic-based dynamic load balancing: Implementation and evaluation. In Voigt et al. [921], pages 920–929. ga96aMunetomo. [545] M. Munir-ul, M. Chowdhury, and Yun Li. Messy genetic algorithm based new learning method for structurally optimised neurofuzzy controllers. In Proceedings of the IEEE International Conference on Industrial Technology, pages 274–278, Shanghai, China, 2.-6. December 1996. IEEE, New York, NY. †CCA54767/97 ga96aMunir-ul. [546] I. B. Ozyurt and A. K. Sunol. Fuzzy genetic algorithm based inductive learning system (FGALS): a new machine learning approach and application for chemical process fault diagnosis. In Proceedings of the Application of Artificial Intelligence in Engineering XI, page 27, Clearwater, FL (USA), September 1996. Comput. Mech. Publications, Southampton (UK). †CCA60561/97 ga96aOzyurt. [547] Constantinos S. Pattichis and Christos N. Schizas. Genetic-based machine learning for the assessment of certain nuromuscular disorders. IEEE Transactions on Neural Networks, 7(2):427–439, March 1996. ga96aPattichis. [548] H. Pereira, N. Gracias, J. A. Lima, and A. Rosa. Solving the XOR problem using genetic algorithms and unsupervised learning. In Proceedings of the World Congress on Neural Networks, pages 568–573, San Diego, CA (USA), 15.-18. September 1996. Lawrence Erlbaum Assoc. (Mahwah, NJ, USA). †CCA51094/98 ga96aPereira. [549] Phil Mars, J. R. Chen, and Raghu Nambiar. chapter 4. Genetic and evolutionary optimisation, pages 53–140. CRC Press, Boca Raton, FL, 1996. ga96aPhilMars. [550] Q. F. Zhao. Co-evolutionary learning of neural networks. Journal of Intelligent & Fuzzy Systems, 6(1):83–90, 1996. †P81724 ga96aQFZhao. [551] Qiangfu Zhao and Tatsuo Higuchi. Evolutionary learning of nearest-neighbor MLP. IEEE Transactions on Neural Networks, 7(3):762–767, May 1996. ga96aQZhao. [552] Joshi Rajani R and K. Krishnanand. Probabilistic learning in immune network: weighted tree matching model. Journal of Computational Biology, 3(1):143–162, Spring 1996. ga96aRRJoshi. [553] R. W. Smalz and M. Conrad. High level control of evolutionary learning in complex neural architectures: the evolutionary credit apportionment approach. In ?, editor, Proceedings of the Fifth Annual Conference on Evolutionary Programming, pages 197–206, San Diego, CA (USA), 29. February- 3. March 1996. MIT Press, Cambridge, MA. †CCA99048/97 ga96aRWSmalz. [554] L. Magnus Rattray and Jonathan Shapiro. The dynamics of a genetic algorithm for a simple learning problem. J. Phys. A. Math. Gen. (UK), 29(23):7451–7473, 1996. ga96aRattray. [555] Steve G. Romaniuk. chapter 9. Learning to learn with evolutionary growth perceptrons, page ? 1996. †toc ga96aRomaniuk. [556] Marco Russo. A genetic approach to fuzzy learning. In Proceedings of the 1st International Symposium on Neuro-Fuzzy Systems, AT ’96, pages 9–16, Lausanne (Switzerland), 29.-31. August 1996. IEEE, New York, NY. †CCA53764/97 ga96aRusso. [557] S. M. Lucas. Evolving neural network learning behaviours with set-based chromosomes. In ?, editor, Proceedings of the 4th European Symposium on Artificial Neural Networks, volume ?, pages 291–296, Bruges, Belgium, 24.-26. April 1996. D Facto, Brussels. †CCA87097/96 ga96aSMLucas. [558] Seung-Soo Han and G. S. May. Optimization of neural-network structure and learning parameters using genetic algorithms. In Proceedings of the International Test Conference 1996, volume ?, page ?, Washington, DC, 20.-25. October 1996. IEEE Computer Society Press, Los Alamitos, CA. †P73308 ga96aSSHan. [559] M. Sase, Y. Yamagata, and Y. Kosugi. Query-based neural network learning using genetic algorithm. Transactions of the Institute of Electronics, Information, and Communication Engineers D-II (Japan), J79D-II(5):960–968, 1996. In Japanese †CCA56311/96 ga96aSase. [560] Charles. C. W. Sullivan and Anthony G. Pipe. Efficient evolution strategies for exploration in mobile robotics. In Fogarty? [944], pages 245–259. ga96aSullivan.

62


[561] M. Tabuchi and T. Taura. Methodology for interactive knowledge acquisition between genetic learning engine and human. J. Jpn. Soc. Artif. Intell. (Japan), 11(4):600–607, 1996. In Japanese †CCA82994/96 ga96aTabuchi. [562] T. Terano and Yoko Ishino. Knowledge acquisition from questionnaire data using simulated breeding and inductive learning methods. In Proceedings of the Third World Congress on Expert Systems, volume 2, pages 869–876, Seoul (South Korea), 5.-9. February 1996. Cognizant Communications Corp., New York, NY (USA). †CCA31191/97 ga96aTerano. [563] Y. H. Zhou, Z. H. Zhang, Y. C. Lu, and C. Y. Shi. Multistrategy learning using genetic algorithms and neural networks for pattern-classification. In Proceedings of the Information Intelligence and Systems, volume 1, pages 1686–1689. [564] Y. Liu and Xin Yao. A population-based learning algorithm which learns both architectures and weights of neural networks. Chinese Journal of Advanced Software Research, 3(1):?, ? 1996. (to appear) †News /Yao ga96aYLiu. [565] Yao Susu, Wei Chengjian, and He Zhenya. An adaptive fuzzy neural network with evolutionary learning algorithm. In Proceedings of the International Conference on Neural Information Processing, volume 2, pages 845–850, Hong Kong, 24.-27. September 1996. Springer-Verlag, Singapore. †CCA18868/97 ga96aYSusu. [566] Abdul Rahman Bohari and Naoki Mizuno. Learning and structural design of feedforward neural networks by employing genetic algorithms. In Proceedings of the 1996 35th SICE Annual Conference, SICE’96, pages 1377–1382, Tottori, Japan, 24.-26. July 1996. Society of Instrument and Control Engineers (SICE), Tokyo (Japan). †EI M004923/97 ga96bBohari. [567] Royce O. Bowden and Stanley F. Bullington. Development of manufacturing control strategies using unsupervised machine learning. IIE Transactions, 28(4):319–331, 1996. †EI M084326/96 ga96bBowden. [568] C. F. Eick, Yeong-Joon Kim, N. Secomandi, and E. Toto. DELVAUX-an environment that learns Bayesian rule-sets with genetic algorithms. In Proceedings of the Third World Congress on Expert Systems, volume 2, pages 758–765, Seoul (South Korea), 5.-9. February 1996. Cognizant Communications Corp., New York (USA). †CCA31183/97 ga96bCFEick. [569] Jean-Yves Donnart and Jean-Arcady Meyer. Learning reactive and planning rules in a motivationally autonomous animat. IEEE Transactions on Systems, Man, and Cybernetics, 26(3):381–395, 1996. ga96bDonnart. [570] G. H. Kim and C. S. G. Lee. Genetic reinforcement learning for scheduling heterogeneous machines. In Proceedings of the 1996 IEEE International Conference on Robotics and Automation, volume 3, pages 2798– 2803, Minneapolis, MN, 22.-28. April 1996. IEEE, New York, NY. †CCA70311/96 ga96bGHKim. [571] E. Gelenbe. Closed form solutions for learning random genetic algorithms. In Proceedings of the International Neural Network Society 1996 Annual Meeting, pages 1071–1075, San Diego, CA, 15.-18. September 1996. Lawrence Erlbaum Assoc, Mahwah, NJ (USA). †CCA51133/98 ga96bGelenbe. [572] Heung Bum Kim, Sung Hoon Jung, Tag Gon Kim, and Kyu Ho Park. Fast learning method for backpropagation neural network by evolutionary adaptation of learning rates. Neurocomputing (Netherlands), 11(1):101–106, 1996. †CCA61237/96 ga96bHBKim. [573] Naohiro Hondo, Hitoshi Iba, and Yukinori Kakazu. Robust GP in robot learning. In Voigt et al. [921], pages 751–760. ga96bHondo. [574] Pedro Larra˜ naga, Mikel Poza, Yosu Yurramendi, Roberto H. Murga, and Cindy M. H. Kuijpers. Structure learning of Bayesian networks by genetic algorithms: a performance analysis of control parameters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(9):912–926, September 1996. ga96bLarranaga. [575] Derek A. Linkens and H. Okola Nyongesa. Learning systems in intelligent control: an appraisal of fuzzy, neural and genetic algorithm control applications. IEE Proceedings, Control Theory Appl., 143(4):367–386, 4. July 1996. * CCA 77845/96 EI M160405/96 ga96bLinkens. [576] M. H. Lim, S. Rahardja, and B. H. Gwee. A GA paradigm for learning fuzzy rules. Fuzzy Sets and Systems, 82(2):177–186, 1996. * CCA77849/96 ga96bMHLim. [577] Man Leung Wong and Kwong Sak Leung. Learning recursive functions from noisy examples using genetic programming. In Proceedings of the First Annual Conference, pages 238–246, Stanford, CA, 28.-31. July 1996. MIT Press, Cambridge, MA. †CCA49651/97 ga96bMLWong. [578] R. Andrew McCallum. Hidden state and reinforcement learning with instance-based state identification. IEEE Transactions on Systems, Man, and Cybernetics, 26(3):464–473, 1996. ga96bMcCallum.

Bibliography

63

[579] Masaharu Munetomi, Yoshiaki Takai, and Yoshiharu Sato. An application of a genetic algorithm to stochastic learning. Transactions of the Institute of Electronics, Information, and Communication Engineers D-II (Japan), J79D-II(2):230–238, 1996. †CCA37527/96 ga96bMunetomo. [580] Q. Zhao. Co-evolutionary algorithm: a new approach to evolutionary learning. In Proceedings of the 4th International Conference on Soft Computing, volume 2, pages 529–532, Fukuoka, Japan, 30. sep- 5. oct ? 1996. World Scientific, Singapore. †CCA53831/97 ga96bQZhao. [581] R. M. Kil and Sang-Hoon Oh. Supervised learning with genetic algorithm. In Proceedings of the International Neural Network Society 1996 Annual Meeting, pages 1082–1087, San Diego, CA, 15.-18. September 1996. Lawrence Erlbaum Assoc, Mahwah, NJ (USA). †CCA51135/98 ga96bRMKil. [582] Magnus Rattray and Jonathan Shapiro. Noisy fitness evaluation in genetic algorithms and the dynamics of learning. In Richard K. Belew and Michael D. Vose, editors, Proceedings of the fourth Foundations of Genetic Algorithms Workshop, pages 117–140, Alcala Park, San Diego, 2.-5. August 1996. Morgan Kauffman, San Mateo, CA. † ga96bRattray. [583] S. J. Louis. Working from blueprints - evolutionary learning for design. Artificial Intelligence in Engineering (UK), 11(3):335, 1996. †P75496 ga96bSJLouis. [584] Sung-Bae Cho and Seung-Ik Lee. Hybrid evolutionary learning of fuzzy logic and genetic algorithm. In Proceedings of the First Asia-Pacific Conference, pages 206–215, Taejon (Korea), 9.-12. November 1996. Springer-Verlag, Berlin (Germany). †CCA81827/97 ga96bSung-Cho. [585] T. Terano and Yoko Ishino. Knowledge acquisition from questionnaire data using simulated breeding and inductive learning methods. Expert Systems Applications (UK), 11(4):507–518, 1996. †CCA24226/97 ga96bTerano. [586] E. Tunstel, M.-R. Akbarzadeh-T., Kishan K. Kumbla, and Mohammad Jamshidi. Soft computing paradigms for learning fuzzy controllers with applications to robotics. In Proceedings of the 1996 Biennial Conference of the North American Fuzzy Information Processing Society - NAFIPS, pages 355–359, Berkeley, CA, 19.-22. June 1996. IEEE, New York, NY. * EI M142790/96 CCA78815/96 ga96bTunstel. [587] Herbert Dawid. Learning of cycles and sunspot equilibria by genetic algorithms. Journal of Evolutionary Economics, 6(?):361–373, ? 1996. ga96cHDawid. [588] H. Tamaki, M. Ochi, and M. Araki. Application of genetics-based machine learning to production scheduling. In ?, editor, Proceedings of the Japan-USA Symposium on Flexible Automation, volume 2, pages 1221–1224, Boston, MA, 7.-10. July 1996. ASME, New York, NY. †CCA97956/97 ga96cHTamaki. [589] Hisao Ishibuchi and Tadahiko Murata. A learning method of fuzzy classification rules by a genetic algorithm. Transactions of the Institute of Electronics, Information, and Communication Engineers A (Japan), J79A(7):1289–1297, 1996. (In Japanese) †CCA 77984/96 ga96cIshibuchi. [590] Masaharu Munetomo, Yoshiaki Takai, and Yoshiharu Sato. StGA: an application of a genetic algorithm to stochatic learning automata. Syst. Comput. Jpn. (USA), 27(10):68–78, 1996. †CCA97215/96 ga96cMunetomo. [591] Paul J. Darwen and Xin Yao. Automatic modularization by speciation. In Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, volume ?, page ?, ?, ? 1996. IEEE, Piscataway, NJ. ga96cPJDarwen. [592] Qiangfu Zhao and Tatsuo Higuchi. Efficient learning of NN-MLP based on individual evolutionary algorithm. Neurocomputing (Netherlands), 13(2-4):201–215, 1996. †EI M009225/97 ga96cQZhao. [593] Shyh-Jier Huang and Chuan-Chang Hung. Contour refinement by enhanced query-based learning. In Proceedings of the 1996 IEEE International Symposium on Circuits and Systems, ISCAS, volume 2, pages 616–619, Atlanta, GA, 12.-15. May 1996. IEEE, Piscataway, NJ. †EI M162667/96 ga96cS-JHuang. [594] Brian Carse and Terence C. Fogarty. Fast evolutionary learning of minimal radial basis function neural networks using a genetic algorithm. In Fogarty? [944], pages 18–33. ga96dCarse. [595] Herbert Dawid. Genetic algorithms as a model of adaptive learning in economic systems. Central European Journal for Operations Research and Economics, 4(1):7–23, ? 1996. ga96dHDawid. [596] Qiangfu Zhao. A study on co-evolutionary learning of neural networks. In ?, editor, Proceedings of the First Asia-Pacific Conference, Simulated Evolution and Learning, pages 116–125, Taejon (Korea), 9.-12. November 1996. Springer-Verlag, Berlin (Germany). †CCA79343/97 ga96dQiangfuZhao. [597] Herbert Dawid and A. Mehlmann. Genetic learning in strategic form games. Complexity (USA), 1(5):51–59, 1996. †CCA 51611/96 ga96eHDawid.

64


[598] Qiangfu Zhao. Hierarchical evolutionary algorithm based on IEA and its application to structural learning. In Proceedings of the International Neural Network Society 1996 Annual Meeting, pages 1076–1018, San Diego, CA, 15.-18. September 1996. Lawrence Erlbaum Assoc, Mahwah, NJ (USA). †CCA51134/98 ga96eQiangfuZhao. [599] Brian Carse and Terence C. Fogarty. Fast evolutionary learning of minimal radial basis function neural networks using a genetic algorithm. In ?, editor, Proceedings of the Evolutionary Computing, volume ?, pages 1–22, Brighton, UK, 1.-2. April 1996. Springer-Verlag, Berlin (Germany). †CCA94886/96 ga96fCarse. [600] Hitoshi Iba, M. Iwata, and T. Higuchi. Machine learning approach to gate-level evolvable hardware. In Proceedings of the First International Conference, pages 327–343, Tsukuba, Japan, 7.-8. October 1996. Springer-Verlag, Berlin (Germany). †CCA73792/97 ga96gHIba. [601] A. D. Brown and H. C. Card. Evolutionary artificial neural networks for competitive learning. In Proceedings of the 1997 IEEE International Conference on Neural Networks, volume 3, pages 1558–1562, Houston, TX, 9.-12. June 1997. IEEE, New York, NY. †CCA79080/97 ga97aADBrown. [602] A. Ugur and Michael Conrad. Structuring pattern generalization through evolutionary techniques. In Proceedings of the 6th International Conference, Evolutionary Programming VI, pages 311–321, Indianapolis, IN, 13.-16. April 1997. Springer-Verlag, Berlin (Germany). †CCA70665/97 ga97aAUgur. [603] J. Aguilar and A. Colmenares. Recognition algorithm using evolutionary learning on the random neural networks. In Proceedings of the 1997 IEEE International Conference on Neural Networks, volume 2, pages 1023–1028, Houston, TX, 9.-12. June 1997. IEEE, New York, NY. †CCA79366/97 ga97aAguilar. [604] A. Ambainis, R. Desper, M. Farach, and S. Kannan. Nearly tight bounds on the learnability of evolution. In Proceedings of the 38th Annual Symposium on Foundations of Computer Science, pages 524–533, Miami Beach, FL, 20.-22. October 1997. IEEE Computer Society Press, Los Alamitos , CA. †CCA9742/98 ga97aAmbainis. [605] B. Chandler, C. Rekeczky, Y. Nishio, and A. Ushida. Using adaptive simulated annealing in CNN template learning-a powerful alternative to genetic algorithms. In Proceedings of the 1997 European Conference on Circuit Theory and Design, volume 2, pages 655–660, Budapest (Hungary), 31. aug- 3. sept ? 1997. Tech. Univ. Budabest, Budabest (Hungary). †CCA77477/99 ga97aBChandler. [606] Jerzy W. Bala, Kenneth A. De Jong, J. Huang, Haleh Vafaie, and Harry Wechsler. Using learning to facilitate the evolution of features for recognizing visual concepts. Evolutionary Computation, 4(3):?, 1997. (Journal: available via www URL: http://www-mitpress.mit.edu/jrnls-catalog/evolution.html) †toc ga97aBala. [607] Chih-Kuan Chiang, Hung-Yuan Chung, and Jin-Jye Lin. A self-learning fuzzy logic controller using genetic algorithms with reinforcements. IEEE Transactions on Fuzzy Systems, 5(3):460–467, August 1997. ga97aC-KChiang. [608] C. Gegout. Evolutionary learning of recurrent networks by succestive orthogonal inverse approximations. In George D. Smith and Nigel C. Steele, editors, Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pages 378–383, Norwich, UK, 2.-4. April 1997. Springer-Verlag, Berlin. ga97aCGegout. [609] Chae Y. Lee, S. Piramuthu, and Y.-K. Tsai. Job shop scheduling with a genetic algorithm and machine learning. Int. J. Prod. Res. (UK), 35(4):1171–1191, 1997. †CCA35257/97 ga97aCLee. [610] Claudio De Stefano and Angelo Marcelli. Generalization vs. specialization: quantitative evaluation criteria for genetics-based learning systems. In Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics, volume 3, pages 2865–2870, Orlando, FL, 12.-15. October 1997. IEEE, New York, NY. †CCA613/98 ga97aCdeStefano. [611] Chen Enhong, Wang Qingyi, Gu Zhenmei, and Cai Qingsheng. Learning fault diagnosis knowledge for an FCC expert system by genetic algorithms. High Technol. Lett. (China), 3(2):22–26, 1997. †CCA31276/98 ga97aChEnhong. [612] K. J. Chisholm and P. V. G. Bradbeer. Machine learning using a genetic algorithm to optimise a draughts program board evaluation function. In Proceedings of 1997 IEEE International Conference on Evolutionary Computation, pages 715–720, Indianapolis, IN, 13.-16. April 1997. IEEE, New York, NY. †CCA52963/97 ga97aChisholm. [613] Claudio DeStefano and Angelo Marcelli. Generalization vs. specialization: Quantitative evaluation criteria for genetics-based learning systems. page 2865.

Bibliography

65

[614] Dong Wook Lee and Kwee B. Sim. Behavior learning and evolution of collective autonomous mobile robots using reinforcement learning and distributed genetic algorithms. J. Korea Inst. Telem. Electron. S (South Korea), 34-S(8):56–64, 1997. In Korean †CCA11877/98 ga97aDongWLee. [615] W. Dwinnell. An evolutionary learning tool. ga97aDwinnell. [616] Jacob Eisenstein. ga97aEisenstein.

PC AI (USA), 11(5):38–40, 1997.

Genetic algorithms and incremental learning.

†CCA93957/97

In Koza [945], page ?

†Koza

[617] F. Neri. REGAL3.2: FOL concept learning by cooperative genetic algorithms. In Proceedings of the 5th Congress of the Italian Association for Artificial Intelligence, pages 443–446, Rome, Italy, 17.-19. September 1997. Springer-Verlag, Berlin (Germany). †CCA9502/98 ga97aFNeri. [618] Gary B. Parker, David W. Braun, and Ingo Cyliax. Learning gaits for the Stiquito. In Proceedings of the 1997 8th International Conference on Advanced Robotics, pages 285–290, Monterey, CA, 7.-9. July 1997. IEEE, Piscataway, NJ. †EI M178207/97 ga97aGaParker. [619] J. Garrido and M. A. Sanz-Bobi. Learning rules from the experience of an expert system using genetic algorithms. In Proceedings of the Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, volume ?, pages 226–231, Glasgow, UK, 2.-4. September 1997. IEE, London, UK. †CCA6172/98 ga97aGarrido. [620] A. Giordana, L. Saitta, and G. LoBello. A coevolutionary approach to concept learning. page 257. [621] Pierre Yves Glorennec and Lionel Jouffe. Fuzzy Q-learning. In Proceedings of the 1997 6th IEEE International Conference on Fuzzy Systems, volume 2, pages 659–662, Barcelona, Spain, 1.-5. July 1997. IEEE, Piscataway, NJ. †EI M007262/98 ga97aGlorennec. [622] J. Goddard, R. Urbieta Parrazales, I. L´ opez, and A. de Luca P. Rule learning in fuzzy systems using evolutionary programs. In Proceedings of the 1997 39th Midwest Symposium on Circuits and Systems, volume 2, pages 703–706, Ames, Iowa, 18.-11. August 1997. IEEE, Piscataway, NJ. ga97aGoddard. [623] Heinrich Braun and Thomas Ragg. Evolutionary optimization of neural networks for reinforsement learning algorithms. In George D. Smith and Nigel C. Steele, editors, Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pages 384–388, Norwich, UK, 2.-4. April 1997. SpringerVerlag. ga97aHBraun. [624] H. Murao and S. Kitamura. Evolution of locally defined learning rules and their coordination in feedforward neural networks. Artif. Life Robot. (Japan), 1(2):89–94, 1997. †CCA77486/99 ga97aHMurao. [625] Heitor S. Lopes, M.S. Coutinho, and W. C. de Lima. An evolutionary approach to simulate cognitive feedback learning in medical domain. In Elie Sanchez, Toshio Shibata, and Lotfi A. Zadeh, editors, Genetic Algorithms and Fuzzy Logic Systems, pages 193–207. World Scientific, Singapore, 1997. †Lopes ga97aHSLopes. [626] H. Takahashi and T. Uchiyama. Biologically plausible and efficient learning. In Proceedings of the 1997 International Conference on Neural Information Processing and Intelligent Information Systems, volume 1, pages 259–262, Dunedin (New Zealand), 24.-28. November 1997. Springer-Verlag, Singapore. †CCA74932/98 ga97aHTakahashi. [627] Vilhelm Heiberg. Learning Bayesian networks using a genetic algorithm. In Koza [945], page ? ga97aHeiberg.

†Koza

[628] Heng-Ming Tai and D. Robinson. Game learning using a genetic algorithm. Int. J. Model. Simul. (USA), 17(4):270–275, 1997. †CCA18036/98 ga97aHeng-MingTai. [629] Hitoshi Iba. Multiple-agent learning for a robot navigation task by genetic programming. In Koza et al. [924], page ? †conf.prog ga97aIba. [630] J. Akec and S. J. Steiner. Genetic-algorithms-based parameter and rule learning for fuzzy logic control systems. In Proceedings of the Fifth International Conference on FACTORY 2000 - The Technology Exploitation Process, volume ?, pages 325–328, Cambridge, UK, 2.-4. April 1997. IEE, London (UK). †CCA91097/97 ga97aJAkec. [631] J. R. Chen, Raghu Nambiar, and Phil Mars. Learning Algorithms: Theory and Applications in Signal Processing, Control and Communications. Electronic Engineering Systems. CRC Press, Boca Raton, FL, 1997. †www.amazon.com ga97aJRChen. [632] John S. Gero and Lan Ding. Learning emergent style using an evolutionary approach. In Blumenstein [946], pages 171–175. ga97aJSGero.

66


[633] Jiancheng Qiu and M. Walters. A GA-based learning algorithm for the learning of fuzzy behaviour of a mobile robot reactive control system. In Proceedings of the Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, pages 251–258, Glasgow (UK), 2.-4. September 1997. IEE, London (UK). †CCA3151/98 ga97aJiancQiu. [634] Jihoon Yang and Vasant Honavar. Feature subset selection using a genetic algorithm. In Koza et al. [924], pages 380–399. ga97aJihoonYang. [635] Kimmo Fredriksson. Learning the structure of neural networks using genetic algorithms. Master’s thesis, University of Helsinki, Department of Computer Science, 1997. †report on activities ga97aKFredriksson. [636] K. Govinda Char. Evolution of structure and learning – a GP approach. In ?, editor, Proceedings of the International Work-Conference on Artificial and Natural Neural Networks, pages 510–517, Lanzarote, Spain, 4.-6. June 1997. Springer-Verlag, Berlin (Germany). †CCA78958/97 ga97aKGovChar. [637] K. W. C. Ku and M. W. Mak. Exploring the effects of Lamarckian and Baldvinian learning in evolving recurrent neural networks. In Proceedings of 1997 IEEE International Conference on Evolutionary Computation, pages 617–621, Indianapolis, IN, 13.-16. April 1997. IEEE, New York, NY. †CCA44863/97 ga97aKWCKu. [638] H. C. Kennedy, C. Chinniah, P. Bradbeer, and L. Morss. The construction and evaluation of decision trees: a comparison of evolutionary and concept learning methods. In ?, editor, Proceedings of the Evolutionary Computing on AISB International Workshop, volume ?, pages 147–161, Manchester, UK, 7.-8. April 1997. Springer-Verlag, Berlin (Germany). †CCA9464/98 ga97aKennedy. [639] A. Likartsis, I. Vlachavas, and Lefteri H. Tsoukalas. A new hybrid neural-genetic methodology for improving learning. In Proceedings of the Ninth IEEE International Conference on Tools with Artificial Intelligence, volume ?, pages 32–36, Newport Beach, CA, 3.-8. November 1997. IEEE Computer Society Press, Los Alamitos, CA. ga97aLikartsi. [640] Tom Longshaw. Evolutionary learning of large grammars. In Koza et al. [924], page ? ga97aLongshaw.

†conf.prog

[641] Sushil John Louis. Working from blueprints: evolutionary learning for design. Artif. Intell. Eng. (UK), 11(3):335–341, 1997. †CCA59761/97 ga97aLouis. [642] M. Koppen, M. Teunis, and B. Nickolay. A neural network that uses evolutionary learning. In Proceedings of 1997 IEEE International Conference on Evolutionary Computation, pages 635–639, Indianapolis, IN, 13.-16. April 1997. IEEE, New York, NY. †CCA49050/97 ga97aMKoppen. [643] G. Mayley. Landscapes, learning costs and genetic assimilation. Evolutionary Computation, 4(3):?, 1997. (Journal: available via www URL: http://www-mitpress.mit.edu/jrnls-catalog/evolution.html) †toc ga97aMayley. [644] Oscar Cord´ on and Francisco Herrera. A three-stage evolutionary process for learning descriptive and approximate fuzzy-logic-controller knowledge bases from examples. Int. J. Approx. Reason. (USA), 17(4):369–407, 1997. †CCA1864/98 ga97aOCordon. [645] Phillip D. Stroud. Learning and adaptation in an airborne laser fire controller. IEEE Transactions on Neural Networks, 8(5):1078–1089, September 1997. ga97aPDStroud. [646] Francisco Pereira and Ernesto Costa. The influence of learning in the optimization of royal road functions. In Pavel Oˇsmera, editor, Proceedings of the 3rd International Mendel Conference on Genetic Algorithms, Optimization problems, Fuzzy Logic, Neural networks, Rough Sets (MENDEL’97), pages 244–249, Brno (Czech Republic), 25.-27. June 1997. Technical University of Brno. ga97aPereira. [647] Qiangfu Zhao. A co-evolutionary algorithm for neural network learning. In Proceedings of the 1997 IEEE International Conference on Neural Networks, volume 1, pages 432–437, Houston, TX, 9.-12. June 1997. IEEE, New York, NY. †CCA78928/97 ga97aQiangfuZhao. [648] R. Franke. Behavioral heterogeneity and genetic algorithm learning in the cobweb model. Discussion Paper 9, Universit¨ at Bremen, IKSF - Fackbereich 7 - Wirtschaftswissenschaft, 1997. †[337] ga97aRFranke. [649] R. Kumar and P. Rockett. Multi-objective genetic algorithm partitioning for hierarchical learning of high dimensional spaces. In Proceedings of the IEE Colloquium on Pattern Recognition, pages 6/1–6/6, London (UK), 26. February 1997. IEE, London, UK. †CCA54058/97 ga97aRKumar. [650] Ra´ ul Pérez. Aprendizaje de reglas difusas usando algoritmos genéticos [Concept Learning trought Genetic Algorithms]. PhD thesis, University of Granada, Dpto. Ciencias de la Computaci´ on e Inteligencia Artificial, 1997. (in Spanish) * www /Perez ga97aRaulPerez.

Bibliography

67

[651] Ursula Rost and Peter Oechtering. Knowledge-based genetic learning. In G¨ osta Grahne, editor, Proceedings of the Sixth Scandinavian Conference on Artificial Intelligence, pages 107–118, Helsinki (Finland), 18.20. August 1997. IOS Press. (available via anonymous ftp site ftp.uwasa.fi directory cs/3NWGA file Rost.ps.Z) ga97aRost. [652] Javier Segovia and Pedro Isasi. Genetic programming for designing ad hoc neural network learning rules. In Koza et al. [924], page ? †conf.prog ga97aSegovia. [653] J. W. C. Sullivan and A. G. Pipe. An evolutionary optimisation approach to motor learning with first results of an application to robot manipulators. In Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics, volume 5, pages 4406–4411, Orlando, FL, 12.-15. October 1997. IEEE, New York, NY. †CCA101852/97 ga97aSullivan. [654] Totu Kumagai, Mitsuo Wada, Sadayoshi Mikami, and Ryoichi Hashimoto. Structured learning in recurrent neural network using genetic algorithm with interval copy operator. In Proceedings of 1997 International Conference on Evolutionary Computation, pages 651–656, Indianapolis, IN, 13.-16. April 1997. IEEE, New York, NY. †CCA44868/97 ga97aTKumagai. [655] T. Longshaw. Evolutionary learning of large grammars. In Proceedings of the Second Annual Conference, pages 445–455, Stanford, CA, 13.-16. July 1997. Morgan Kaufmann Publishers, San Francisco, CA (USA). †CCA69041/98 ga97aTLongshaw. [656] Tom M. Mitchell. Does machine learning really work? ga97aTMMitchell.

AI Mag., 18(3):11–20, 1997.

†EI M007294/98

[657] Francesco Vivarelli, Piero Fariselli, and Rita Casadio. The prediction of protein secondary structure with a cascade correlation learning architecture of neural networks. Neural Computat. Appl., 6(1):57–62, ? 1997. * CCA 98973/97 ga97aVivarelli. [658] Wang Tong and Shi Chunyi. EBL/GA: a model of explanation-based learning system. Chin. J. Comput. (China), 20(2):125–132, 1997. In Chinese †CCA44743/97 ga97aWangTong. [659] Xin Yao. Automatic acquisition of strategies by co-evolutionary learning. In Blumenstein [946], pages 23–29. ga97aYao. [660] Yuanhui Zhou, Yuchang Lu, and Chunyi Shi. Combining neural network, genetic algorithm and symbolic learning approach to discover knowledge from databases. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pages 4388–4393, Orlando, FL, 12.-15. October 1997. IEEE, Piscataway, NJ. †A98-27042 ga97aYuanhuiZhou. [661] Yuji Ouchi and Eiichiro Tazaki. Learning and reasoning method using fuzzy coloured Petri nets under uncertainty. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, volume 4, pages 3867–3871, Orlando, FL, 12.-15. October 1997. IEEE, Piscataway, NJ. †A98-26989 ga97aYujiOuchi. [662] Antonio Gonzalez and Raul Perez. Using information measures for determining the relevence of the predictive variables in learning problems. In Proceedings of the 1997 6th IEEE International Conference on Fuzzy Systems, volume 3, pages 1423–1428, Barcelona, Spain, 1.-5. July 1997. IEEE, Piscataway, NJ. †EI M011310/98 ga97bGonzalez. [663] J. Tanomaru. Evolving Turing machines from examples. In Proceedings of the Third European Conference on Artificial Evolution, pages 167–180, Nimes (France), 22.-24. October 1997. Springer-Verlag, Berlin (Germany). †CCA37181/98 ga97bJTanomaru. [664] Hillol Kargupta and Brian Stafford. From DNA to protein: transformations and their possible role in linkage learning. In Thomas B¨ ack, editor, Proceedings of the Seventh International Conference on Genetic Algorithms, pages 409–416, East Lansing, MI, 19.-23. July 1997. Morgan Kaufmann Publishers, Inc., San Francisco, CA. ga97bKargupta. [665] P. Larranaga, B. Sierra, M. J. Gallego, M. J. Michelena, and J. M. Picaza. Learning Bayesian networks by genetic algorithms: a case study in the prediction of survival in malignant skin melanoma. In Proceedings of the 6th Conference on Artificial Intelligence in Medicine Europe, pages 261–272, Grenoble (France), 23.-26. March 1997. Springer-Verlag, Berlin (Germany). †CCA68337/97 ga97bLarranaga. [666] Pablo Funes, Elizabeth Sklar, Hugues Juillé, and Jordan B. Pollack. The Internet as a virtual ecology: coevolutionary arms races between human and artificial populations. Technical Report CS-97-197, Brandeis University, Computer Science Department, 1997. ga97bPabloFunes. [667] Justinian P. Rosca. Hierarchical Learning with Procedural Abstraction Mechanisms. PhD thesis, University of Rochester, 1997. available via anonymous ftp site ftp.cs.rochester.edu directory /pub/u/rosca/gp file jrphdd.ps.gz)* ga97bRosca.

68


[668] Toshio Fukuda and Naoyuki Kubota. Adaptation, learning and evolution for intelligent robotic system. In Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA97, volume ?, pages 204–209, Monterey, CA, 10.-11. July 1997. IEEE Computer Society Press, Los Alamitos , CA. †CCA81615/97 ga97bTFukuda. [669] Y. Wang and Naohiro Ishii. A genetic algorithm for learning weights in a similarity function. In George D. Smith and Nigel C. Steele, editors, Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pages 206–209, Norwich, UK, 2.-4. April 1997. Springer-Verlag, Berlin. ga97bYWang. [670] Xin Yao and Yong Liu. A new evolutionary system for evolving artificial neural networks. IEEE Transactions on Neural Networks, 8(3):694–713, May 1997. ga97bYao. [671] Wilhelmus H. A. M. van den Broek. Chemometrics in spectroscopic near infrared imaging for plastic material recognition. PhD thesis, Catholic University of Nijmegen, 1997. * Geladi ga97bvandenBroek. [672] D. Corne. Comparing a variety of evolutionary algorithm techniques on a collection of rule induction tasks. In George D. Smith and Nigel C. Steele, editors, Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pages 547–550. [673] T. Furuhashi, Y. Miyata, and Y. Uchikawa. A new approach to genetic based machine learning for efficient local improvement and its application to a graphic problem. Inf. Sci. (USA), 103(1-4):87–100, 1997. †CCA98889/97 ga97cFuruhashi. [674] Jorma Laurikkala and Martti Juhola. Learning diagnostic rules from a urological database using genetic algorithm. In Jarmo T. Alander, editor, Proceedings of the Third Nordic Workshop on Genetic Algorithms and their Applications (3NWGA), pages 233–244, Helsinki (Finland), 18.-22. August 1997. Finnish Artificial Intelligence Society (FAIS). (available via anonymous ftp site ftp.uwasa.fi directory cs/3NWGA file Laurikkala.ps.Z) ga97cLaurikkala. [675] Alexander Kel, Andrey Ptitsyn, Vladimir Babenko, Sebastian Meier-Ewert, and Hans Lehrach. A genetic algorithm for designing gene family-specific oligonucleotide sets used for hybridization: the G proteincoupled receptor protein superfamily. Bioinformatics, 14(3):259–270, 1998. ga98aAKel. [676] Angel Kuri. An alternative model of genetic algorithms as learning machines. In Proceedings of the 4th World Congress of Expert Systems - Application of Advanced Information Technologies, pages 898–905, Mexico City, Mexico, 16.-20. March 1998. Cognizant Communications Corp., Elmsford. †P80892 ga98aAKuri. [677] A. Radi and Riccardo Poli. Discovery of backpropagation learning rules using genetic programming. In Proceedings of the IEEE World Congress on Computational Intelligence, pages 371–375, Anchorage, AK (USA), 4.-9. May 1998. IEEE, New York, NY. †CCA75023/98 ga98aARadi. [678] A. V. Spirov and A. S. Kadyrov. Transposon element technique applied to GA-based John Muir’s trail test. In Peter Sloor, Marian Bubak, and Bob Hertzberger, editors, High-Performance Computing and Networking, International Conference and Exhibition, page ?, Amsterdam (The Netherlands), 21.-23. April 1998. Springer Verlag, Berlin. ga98aAVSpirov. [679] J. Aguilar and A. Colmenares. Resolution of pattern recognition problems using a hybrid genetic/random neural network learning algorithm. Pattern Anal. Appl. (UK), 1(1):52–61, 1998. †CCA53592/99 ga98aAguilar. [680] Alan D. Blair, Elizabeth Sklar, and Pablo Funes. Co-evolution, determinism and robustness. In Bob McKay, Xin Yao, Charles S. Newton, Jong-Hwan Kim, and Takeshi Furuhashi, editors, Simulated Evolution and Learning (SEAL-98), volume 1585 of Lecture Notes in Artificial Intelligence, page ?, ?, ? 1998. SpringerVerlag, Berlin. ga98aAlanDBlair. [681] Angel Kuri. An alternative model of genetic algorithms as learning machines. Expert Systems with Applications, 15(?):351–356, ? 1998. ga98aAngelKuri. [682] Haldun Aytug. Genetic learning through simulation: An investigation in shopfloor scheduling. Annals of Operations Research, 78(?):1–29, ? 1998. †toc ga98aAytug. [683] Arkady Borisov and Inta Caunite. Game playing modelling based on the machine learning paradigm. In Oˇsmera [925], pages 155–158. ga98aBorisov. [684] Cristina Cuenca and Jean-Claude Heudin. An agent system for learning profiles in broadcasting applications on the Internet. In J.-K. Hao, E. Lutton, E. Ronald, Marc Schoenauer, and D. Snyers, editors, Artificial Evolution, Third European Conference, AE’97, volume LNCS of 1363, pages 109–122, Nˆımes (France), October 1998. Springer-Verlag Berlin Heidelberg. * www /Springer ga98aCCuenca. [685] Chia-Feng Juang and Chin-Teng Lin. Genetic reinforcement learning through symbiotic evolution for fuzzy controller design. In Proceedings of the 1998 IEEE International Conference on Fuzzy Systems, volume 2, pages 1281–1285, Anchorage, AK, 4.-9. May 1998. IEEE, New York, NY. †CCA85945/98 ga98aChiJuang.

Bibliography

69

[686] Chin-Teng Lin, Chong-Ping Jou, and Cheng-Jiang Lin. GA-based reinforcement learning for neural networks. Int. J. Syst. Sci. (UK), 29(3):233–247, 1998. †CCA34276/98 ga98aChin-TengLin. [687] Daijin Kim and In-Hyun Cho. An optimal design of fuzzy logic controller by co-adaptation of learning and evolution. In Proceedings of the 5th International Conference on Soft Computing and Information/Intelligent Systems, volume 1, pages 454–458, Fukuoka, Japan, 16.-20. October 1998. World Scientific, Singapore. †CCA62066/99 ga98aDaijiKim. [688] I. Dumitrache and C. Buiu. Genetic learning of fuzzy controllers. Mathematics and Computers in Simulation, 49(1-2):13–26, 1998. †CCA78860/99 ga98aDumitrache. [689] Chris Gathercole. An Investigation of Supervised Learning in Genetic Programming. PhD thesis, University of Edinburgh, 1998. (available via www URL: http://www.dai.ed.ac.uk/daidb/papers/documents/pt9810.html) ga98aGathercole. [690] Gyoung Hwan Kim and C. S. George Lee. Genetic reinforcement learning approach to the heterogeneous machine scheduling problem. IEEE Transactions on Robotics and Automation, 14(6):879–893, December 1998. ga98aGyoungHwanKim. [691] Hitoshi Iba. Evolutionary learning of communicating agents. Inf. Sci. (USA), 108(1-4):181–205, 1998. †CCA66068/98 ga98aHIba. [692] H. Tamaki, M. Ochi, and M. Araki. Genetics-based machine learning approach to production scheduling-a case of in-tree type precedence relation. In Proceedings of the IEEE International Symposium om Industrial Electronics, volume 2, pages 714–719, Pretoria, South Africa, 7.-10- July 1998. IEEE, New York, NY. †CCA77286/98 ga98aHTamaki. [693] Heung Bum Kim, Sung Hoon Jung, and Kyu Ho Park. Adaptive learning-rate selection for BPNN using evolutionary programming. J. Electr. Eng. Inf. Sci. (Taiwan), 3(5):551–562, 1998. †CCA72582/99 ga98aHeungKim. [694] Hsinchun Chen, G. Shankaranarayanan, Linlin She, and A. Iyer. A machine learning approach to inductive query by examples: an experiment using relevance feedback, id3, genetic algorithms, and simulated annealing. J. am. Soc. Inf. Sci. (USA), 49(8):693–705, 1998. †CCA55822/98 ga98aHsinChen. [695] I. Burak and L. O. Hall. Chemical process fault diagnosis using kernel retrofitted fuzzy genetic algorithm based learner (FGAL) with a hidden Markov model. In Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, volume 1, pages 190– 199, Benicassim, Spain, 1.-4. June 1998. Springer-Verlag, Berlin (Germany). †CCA77374/98 ga98aIBurak. [696] Takumi Ichimura and Y. Kuriyama. Learning of neural networks with parallel hybrid GA using a royal road function. In Proceedings of the 1998 IEEE International Joint Conference on Neural Networks, volume 2, pages 1131–1136, Anchorage, AK (USA), 4.-9. May 1998. IEEE, New York, NY. †cca84442/98 ga98aIchimura. [697] J. W. Wang, J. S. Pan, C. H. Chen, and H. L. Fang. Wavelet-based signal approximation with multilevel learning algorithms using genetic neuron selection. In Proceedings of the Tenth IEEE International Conference on Tools With Artificial Intelligence, pages 344–353, Taipei, Taiwan, 10.-12. November 1998. IEEE Computer Society Press, Los Alamitos , CA. †P84302 ga98aJWWang. [698] Jong-Chen Chen. Artificial worlds modeling of human resources management through evolutionary learning. In Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, pages 45–50, Anchorage, AK (USA), 4.-9. May 1998. IEEE, New York, NY. †CCA75833/98 ga98aJong-ChenChen. [699] Pavel Kr´ al´ıik and Ivan Bruha. Genetic learner: attribute-based rule-inducing approach. In Oˇsmera [925], pages 45–51. ga98aKralik. [700] T. Kumagai, M. Wada, S. Mikami, and R. Hashimoto. Learning of recurrent neural networks through genetic algorithm with internal copy operator. Transactions of the Institute of Electronics, Information, and Communication Engineers D-II, J81D-II(2):378–385, 1998. (In Japanese) †CCA34302/98 ga98aKumagai. [701] Jorma Laurikkala and Martti Juhola. A genetic-based machine learning system to discover the diagnostic rules for female urinary incontinence. Computer Methods and Programs in Biomedicine, 55(?):217–228, ? 1998. ga98aLaurikkala. [702] N. Ishii and Y. Wang. Learning feature weights for similarity measures using genetic algorithms. In Proceedings of the IEEE International Joint Symposia on Intelligence and Systems, pages 27–33, Rockville, MD (USA), 21.-23. May 1998. IEEE Computer Society Press, Los Alamitos , CA. †P80657 ga98aNIshii.

70


[703] N. Xiong and L. Litz. Learning rule premises containing incomplete structure based on evolutionary algorithm. In Proceedings of the 6th European Congress on Intelligent Techniques and Soft Computing, volume 2, pages 811–815, Aachen (Germany), 7.-10. September 1998. Verlag Mainz, Aachen (Germany). †CCA78866/99 ga98aNXiong. [704] P. Bruhn, Andreas Geyer-Schulz, M. Hahsler, and M. Mottl. Genetic machine learning and (intelligent) Internet agents. OEGAI-J. (Austria), 17(1):18.–25., 1998. In German †CCA63057/98 ga98aPBruhn. [705] Pablo Funes, Elizabeth Sklar, Hugues Juillé, and Jordan B. Pollack. Animal-animat coevolution: using the animal population as fitness function. In R. Pfeifer et al, editor, From Animals to Animats 5: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior, pages 525–533, ?, ? 1998. MIT Press, Cambridge, MA. ga98aPabloFunes. [706] R. Aler, D. Borrajo, and P. Isasi. Genetic programming and deductive-inductive learning: a multi-strategy approach. In Proceedings of the Fifteenth International Conference, pages 10–18, Madison, WI, 24.-27. July 1998. Morgan Kaufmann Publishers, San Francisco, CA. †CCA60862/99 ga98aRAler. [707] Rajeev Kumar and Peter Rockett. Multiobjective genetic algorithm partitioning for hierarchical learning of high-dimensional pattern spaces: a learning-follows-decomposition strategy. IEEE Transactions on Neural Networks, 9(5):822–830, September 1998. ga98aRKumar. [708] Sung Bae Cho and Katsunori Shimohara. Evolutionary learning of modular neural networks with genetic programming. Applied Intelligence, 9(3):191–200, November-December 1998. * PsycINFO2000-03679-001 ga98aSBCho. [709] Sang-Woon Kim, Seong-Hyo Shin, and Y. Aoki. A structural learning of neural-network classifiers using PCA networks and species genetic algorithms. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Science, E81-A(6):1183–1186, 1998. †CCA66290/98 ga98aSang-Kim. [710] D. I. Shapiro and S. L. Girshgorn. One approach to problem of learning on the genetic algorithm and fuzzy categories basis. In Oˇsmera [925], pages 232–239. ga98aShapiro. [711] Elizabeth Sklar and Jordan B. Pollack. Toward a community of evolving learners. In ?, editor, Third International Conference on Learning Sciences (ICLS-98), page ?, ?, ? 1998. ? ga98aSklar. [712] T. Kawakami and Y. Kakazu. A study on autonomous acquisition systems of operation plans-an approach by GA-based machine learning systems. J. Jpn. Soc. Precision Eng. (Japan), 64(5):725–730, 1998. In Japanese †CCA86480/98 ga98aTKawakami. [713] T. Okamoto and T. Matsumoto. Automatic generation of sequencing teaching items using genetic algorithm. J. Jpn. Soc. Artif. Intell. (Japan), 13(1):91–99, 1998. In Japanese †CCA90349/98 ga98aTOkamoto. [714] T. Omata. Learning with assistance based on evolutionary computation computation. In Proceedings of the 1998 IEEE International Conference on Robotics and Automation, volume 3, pages 2180–2185, Leuven (Belgium), 16.-20. May 1998. IEEE, New York, NY. † CCA59229/98 ga98aTOmata. [715] T. Takahama and S. Sakai. Learning rules for fuzzy scaling control by genetic algorithms. Transactions of the Institute of Electronics, Information, and Communication Engineers D-II (Japan), J81D-II(1):119–126, 1998. In Japanese †CCA19370/98 ga98aTTakahama. [716] Meng Xiang-Wu and Cheng Hu. A learning algorithm of Hopfield neural network based on evolutionary programming with forgetting. J. Softw. (China), 9(2):151–155, 1998. (In Chinese) †CCA38287/98 ga98aXiang-Wu. [717] C. Anglano, A. Giordana, G. Lo Bello, and L. Saitta. An experimental evaluation of coevolutive concept learning. In Proceedings of the Fifteenth International Conference, pages 19–27, Madison, WI, 24.-27. July 1998. Morgan Kaufmann Publishers, San Francisco, CA (USA). †CCA60863/99 ga98bAnglano. [718] Sanghamitra Bandyopadhyay, H. Kargupta, and G. Wang. Revisiting the GEMGA: scalable evolutionary optimization through linkage learning. In Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, pages 603–608, Anchorage, AK, 4.-9. May 1998. IEEE, New York, NY. †CCA74633/98 ga98bBandyopadhyay. [719] David B. Fogel, P. J. Angeline, V. W. Porto, E. C. Wasson, III, and E. M. Boughton. Using evolutionary computation to learn about detecting breast cancer. In 1998 IEEE International Conference on Systems, Man, and cybernetics, volume 3, pages 2373–2378, San Diego, CA, USA, 11.-14.October 1998. IEEE, Piscataway, NJ. * www/IEEE ga98bDBFogel. [720] Sevan G. Ficici and Jordan B. Pollack. Challenges in coevolutionary learning: arms-race dynamics, openendedness, and mediocre stable states. In Adami, Belew, Kitano, and Talor, editors, Proceedings of the Sixth International Conference on Artificial Life, page ?, ?, ? 1998. MIT Press, Cambridge, MA. ga98bFicici.

Bibliography

71

[721] Hitoshi Iba. Evolutionary learning of communicating agents. Information Science, 108(1-4):181–205, ? 1998. †www/Iba ga98bHitoshiIba. [722] Julian F. Miller and Peter Thomson. Aspects of digital evolution: geometry and learning. In M. Sipper, D. Mange, and A. Pérez-Uribe, editors, Evolvable Systems: From Biology to Hardware, Second International Conference, ICES 98, volume LNCS of 1478, pages 25–35, Lausanne (Switzerland), September 1998. Springer-Verlag Berlin Heidelberg. * www /Springer ga98bJFMiller. [723] Jorma Laurikkala, Martti Juhola, Jorma Penttinen, and Pauliina Aukee. An internet-based prototype expert system for the diagnosis of female urinary incontinence. In Pasi Koikkalainen and Seppo Puuronen, editors, Human and Artificial Information Processing, Proceedings of the 8th Finnish Artificial Intelligence Conference, pages 98–103, Jyv¨ askyl¨ a (Finland), 7.-9. September 1998. University of Jyv¨ askyl¨ a — Finnish Artificial Intelligence Society. ga98bLaurikkala. [724] Elizabeth Sklar, Alan D. Blair, and Jordan B. Pollack. Co-evolutionary learning: machines and humans schooling together. In ?, editor, Fourth World Congress on Expert Systems, page ?, ?, ? 1998. ? ga98bSklar. [725] Hitoshi Iba. Multi-agent reinforcement learning with genetic programming. In Proceedings of the Annual Conference on Genetic Programming, page ?, ? 1998. †[936] ga98cHitoshiIba. [726] John R. Koza. Using biology to solve a problem in automated machine learning, pages 157–199. Lawrence Erlbaum Associates, Inc., Publishers, Mahwah, NJ, 1998. * PsycINFO1998-07724-005 ga98cJRKoza. [727] Alan D. Blair and Elizabeth Sklar. Exploring evolutionary learning in a simulated hockey environment. In Proceedings of the 1999 Congress on evolutionary Computation-CEC99, volume 1, pages 197–203, Washington, DC, 6.-9. July 1999. IEEE, Piscataway, NJ. †CCA85921/99 ga99aADBlair. [728] Antonio Gonz´ alez and Ra´ ul Pérez. SLAVE: A genetic learning system based on an iterative approach. IEEE Transactions on Fuzzy Systems, 7(2):176–191, April 1999. ga99aAGonzalez. [729] A. Ratle. Optimal sampling strategies for learning a fitness model. In Proceedings of the 1999 Congress on Evolutionary Computation-CEC99, volume 3, pages 2078–2085, Washington, DC, 6.-9. July 1999. IEEE, Piscataway, NJ. †CCA83617/99 ga99aARatle. [730] Igor Aleksander. ga99aAleksander.

Evolutionary checkers.

Nature, 402(6764):857,859–860, 23./30. December 1999.

[731] Cao Xian Bin, Gao Jun, and Wang Xu Fa. An ecological competition model for genetic reinforcement learning. J. Softw. (China), 10(6):658–662, 1999. In Chinese †CCA77269/99 ga99aCaoXiBin. [732] B. Chandler, C. Rekeczky, Y. Nishio, and A. Ushida. CNN template learning using adaptive simulated annealing-performance comparison with a genetic algorithm. Bull. Fac. Eng. Univ. Tokushima (Japan), (44):81–93, 1999. In English †CCA60756/99 ga99aChandler. [733] Cheng-Jian Lin. Reinforcement learning for GA-based neural networks. J. Chin. Inst. Electr. Eng. (Taiwan), 6(2):141–156, 1999. †CCA86488/99 ga99aChengLin. [734] Chi-Sun Joung, Dong Wook Lee, Hyo-Byung Jun, and Kwee Bo Sim. Evolution of neural network’s structure and learning patterns based on competitive co-evolutionary method. J. Inst. Electron. Eng. Korea S (South Korea), 36-S(1):29–37, 1999. In Korean †CCA53479/99 ga99aChiJoung. [735] Chin-Teng Lin and Chong-Ping Jou. Controlling chaos by GA-based reinforcement learning neural network. IEEE Transactions on Neural Networks, 10(4):846–859, July 1999. ga99aChin-TengLin. [736] I. Dumitrache and C. Buiu. Genetic learning of fuzzy controllers. Mathematics and Computers in Simulation, 49(1-2):13–26, 1999. †CCA78860/99 ga99aDumitrache. [737] Eleazar Eskin and Eric Siegel. Genetic programming applied to Othello: introducing students to machine learning research. In The Proceedings of the thirtieth SIGCSE Technical Symposium on Computer Science Education, pages 242–246, New Orleans, LA, ? 1999. ACM, New York. †ACM /www ga99aEEskin. [738] Bogdan Filipiˇc, Tanja Urbanˇciˇc, and V. Krizman. A combined machine learning and genetic algorithm approach to controller design. Engineering Applications of Artificial Intelligence, 12(4):401–409, 1999. †CCA93530/99 ga99aFilipic. [739] Josep Maria Garrell i Guiu, E. Golobardes i Ribé, Ester Bernad´ o i Mansilla, and X. Llor` a i F` abrega. Automatic diagnosis with genetic algorithms and case-based reasoning. Artificial Intelligence in Engineering, 13(?):367–372, ? 1999. ga99aGarrel-Guiu. [740] Herbert Dawid. Adaptive learning by genetic algorithms. Analytical results and applications to economics models. Second edition. Springer-Verlag, Berlin, 1999. †www /MathRev2000e:91025 ga99aHDawid.

72


[741] H. Hagras, V. Callaghan, M. Colley, and M. Carr-West. A fuzzy-genetic based embedded-agent approach to learning and control in agricultural autonomuos vehicles. In Proceedings of the 1999 IEEE International Conference on Robotics and Automation, volume 2, pages 1005–1010, Detroit, MI (USA), 10.-15. May 1999. IEEE, Piscataway, NJ. †CCA79131/99 ga99aHHagras. [742] Hugues Juillé. Methods for Statistical Inference: Extending the Evolutionary Computation Paradigm. PhD thesis, Brandeis University, Department of Computer Science, 1999. ga99aJuille. [743] K. Mehta and S. Bhattacharyya. Combining rules learnt using genetic algorithms for financial forecasting. In Proceedings of the 1999 Congress on Evolutionary Computation-CEC99, volume 2, pages 1245–1252, Washington, DC, 6.-9. July 1999. IEEE, Piscataway, NJ. †CCA83834/99 ga99aKMehta. [744] Kim Wing C. Ku, Man Wai Mak, and Wan Chi Siu. Adding learning to cellular genetic algorithms for training recurrent neural networks. IEEE Transactions on Neural Networks, 10(2):239–252, March 1999. ga99aKimWingCKu. [745] Teuvo Kohonen. Fast evolutionary learning with batch-type self-organizing maps. Neural Processing Letters, 9(2):153–162, April 1999. †CCA 68477/99 ga99aKohonen. [746] Luigi Barone and Lyndon While. An adaptive learning model for simplified poker using evolutionary algorithms. In Proceedings of the 1999 Congress on Evolutionary Computation-CEC99, volume 1, pages 153–160, Washington, DC, 6.-9. July 1999. IEEE, Piscataway, NJ. †CCA85920/99 ga99aLBarone. [747] Liviu I Voicu, Ronald Patton, and Harley R. Myler. Detection performance prediction on IR images assisted by evolutionary learning. In Wendell R. Watkins, Dieter Clement, and William R. Reynolds, editors, Targets and Backgrounds: Characterization and Representation V, volume SPIE-3699, pages 282–292, ?, July 1999. The International Society for Optical Engineering. * www/SPIE Web ga99aLIVoicu. [748] M. Bernaschi, F. Castiglione, P. Seiden, and S. Succi. Learning cascade in the immune system dynamics: a numerical study. Computer Physics Communications, 121-122(xxi-xxxvi):122–125, 1999. (Proceedings of the Europhysics Conference on Computational Physics, CCP 1998) ga99aMBernaschi. [749] Masakatsu Kaneko and Shinji Aoki. Offline teaching methid, 1999. (JP patent no. 11085244. Issued March 30 1999) * fi.espacenet.com ga99aMKaneko. [750] W. G. Macready and B. S. Levitan. Learning landscapes: regression on discrete spaces. In Proceedings of the 1999 Congress on Evolutionary Computation-CEC99, volume 1, pages 687–694, Washington, DC, 6.-9. July 1999. IEEE, Piscataway, NJ. †CCA77229/99 ga99aMacready. [751] V. V. Miagkikh and W. F. Puch. Global search in combinatorial optimization using reinforcement learning algorithms. In Proceedings of the 1999 Congress on Evolutionary Computation-CEC99, volume 1, pages 189–196, Washington, DC, 6.-9. July 1999. IEEE, Piscataway, NJ. †CCA77168/99 ga99aMiagkikh. [752] M. Oliveira, J. Barreiros, E. Costa, and F. Pereira. Lambada: an artificial environment to study the interaction between evolution and learning. In Proceedings of the 1999 Congress on Evolutionary ComputationCEC99, volume 1, pages 145–152, Washington, DC, 6.-9. July 1999. IEEE, Piscataway, NJ. †CCA77547/99 ga99aOliveira. [753] Paul J. Darwen and Jordan B. Pollack. Co-evolutionary learning on noisy tasks. In Proceedings of the 1999 Congress on Evolutionary Computation-CEC99, volume 3, pages 1724–1731, Washington, DC, 6.-9. July 1999. IEEE, Piscataway, NJ. †CCA83605/99 ga99aPJDarwen. [754] T. Sawargi, N. Tani, and O. Katai. Evolutional concept learning from observations through adaptive feature selection and GA-based feature discovery. J. Intell. Fuzzy Syst. (Netherlands), 7(3):239–256, 1999. †CCA86550/99 ga99aSawargi. [755] R. J. Stonier. Evolutionary learning of fuzzy logic controllers over a region of initial states. In Proceedings of the 1999 congress on Evolutionary Computation-CEC99, volume 3, pages 2131–2138, Washington, DC, 6.-9. July 1999. IEEE, Piscataway, NJ. †CCA78880/99 ga99aStonier. [756] Thomas Riechmann. Learning and behavioral stability - an economic interpretation of genetic algorithms. Journal of Evolutionary Economics, 9(?):225–242, ? 1999. †[?] ga99aThomasRiechmann. [757] Wolfgang Banzhaf, Peter Nordin, and Frank D. Francone. Computer implemented machine learning and control system, 1999. (U. S. patent no. 5,946,673. Issued August 31 1999) * fi.espacenet.com ga99aWBanzhaf. [758] Yan Wu and Hong Bao Shi. A fast learning method for fuzzy systems based on genetic algorithms and the gradient descent method. J. Shanghai Tiedao Univ., 20(6):16–20, ? 1999. (in Chinese) †www /MathRev2000j:68160 ga99aYWu.

Bibliography

73

[759] Ivan Zelinka and Jouni Lampinen. An evolutionary learning algorithms for neural networks. In Proceedings of the 5th International Conference on Soft Computing, pages 410–414, Brno (Czech Republic), 9.-12. June 1999. Faculty of Mechanical Engineering, Brno University of Technology. ga99aZelinka. [760] Zhenya He, Chengjian Wei, Bingyao Jin, Wenjiang Pei, and Luxi Yang. A new population-based incremental learning method for the traveling salesman problem. In Proceedings of the 1999 Congress on Evolutionary Computations-CEC99, volume 2, pages 1152–1156, Washington, DC, 6.-9. July 1999. IEEE, Piscataway, NJ. †CCA77177/99 ga99aZhenyaHe. [761] Zhiwei Fu. Dimensionality optimization by heuristic greedy learning vs. genetic algorithms in knowledge discovery and data mining. Intell. Data Anal. (Netherlands), 3(3):211–225, 1999. †CCA91956/99 ga99aZhiweiFu. [762] R. Kumar. On generalisation of machine learning with neural-evolutionary computations. In Proceedings Third International Conference on Computational Intelligence and Multimedia Applications, pages 112–116, New Delhi, India, 23.-26. September 1999. IEEE Computer Society Press, Los Alamitos , CA. †CCA91974/99 ga99bRKumar. [763] S. Chen, Y. Wu, and B. L. Luk. Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks. IEEE Trans. Neural Netw. (USA), 10(5):1239–1243, 1999. †CCA86492/99 ga99bSChen. [764] K. Weicker and N. Weicker. On the improvement of coevolutionary optimizers by learning variable interdedependencies. In Proceedings of the 1999 Congress on Evolutionary Computation-CEC99, volume 3, pages 1627–1632, Washington D.C., 6.-9. July 1999. IEEE, Piscataway, NJ. †CCA77377/99 ga99cWeicker. [765] Raed Abdullah Abu Zitar. Machine learning with rule extraction by genetic assisted reinforcement REGAR: Application to nonlinear control. PhD thesis, Wayne State University, 1993. * DAI Vol. 55 No. 2 ga:AbuZitarThesis. [766] Adel M. Abunawass. Biologically based machine learning paradigms: An introductory course. SIGCSE Bulletin, 24(1):87–91, March 1992. * EI M048287/93 ga:Abunawass92a. [767] David H. Ackley and Michael L. Littman. Interactions between learning and evolution. In Langton et al. [947], pages 487–509. ga:Ackley92a. [768] Chris Adami. Learning and complexity in genetic auto-adaptive systems. Technical Report MAP-164, California Institute of Technology, Pasadena, 1993. (to appear in Complex Systems; available via anonymous ftp site ftp.krl.caltech.edu directory /pub/avida file gaas.ps.gz) ga:Adami93a. [769] Jasmina Arifovic. Learning by genetic algorithms in economic environments. Technical Report Report SFI-90-001, Santa Fe Institute, 1990. * Chattoe/bib ga:Arifovic90a. [770] Marc C. Atkin and Paul R. Cohen. Genetic programming to learn an agent’s monitoring strategy. Technical report TR-93-26, University of Massachusetts, Amherst, 1993. †Langdon ga:Atkin93b. [771] Byoung-Tak Zhang. Learning by genetic neural evolution. Informatik Berichte 93, University of Bonn, 1992. †Muhlenbein93/GA5 ga:B-TZhang92a. [772] Frank Hoffmeister and Thomas B¨ ack. Genetic self-learning. In Varela and Bourgine [932], pages 227–235. ga:Back91c. [773] Thomas B¨ ack. Adaptive learning by evolutionary algorithms within search spaces of different complexity. In P. Bock, M. Loew, F. J. Radermacher, and M. M. Richter, editors, Proceedings of the 3rd International FAW Workshop on Adaptive Learning and Neural Networks, pages 1–17, Schloß Reisensburg, G¨ unzburg (Germany), 2.-7. July 1992. Research Institute for Applied Knowledge Engineering (FAW), Ulm. †UDO RA ga:Back92f. [774] Richard K. Belew. When both individuals and populations search: Adding simple learning to the genetic algorithm. In Schaffer [907], pages 34–41. ga:Belew89a. [775] Richard K. Belew. Evolution, learning, and culture: Computational metaphors for adaptive algorithms. Complex Systems, 4(1):11–49, February 1990. ga:Belew90a. [776] Christian Blume. Gleam — a system for simulated ‘intuitive learning’. In Hans-Paul Schwefel and R. M¨ anner, editors, Parallel Problem Solving from Nature, volume 496 of Lecture Notes in Computer Science, pages 48–54, Dortmund (Germany), 1.-3. October 1991. Springer-Verlag, Berlin. ga:Blume90. [777] Pierre Bonelli, Alexandre Parodi, Sandip Sen, and Stewart W. Wilson. Newboole: A fast GBML system. In Bruce Porter and Raymond Mooney, editors, Machine Learning: Proceedings of the Seventh International Conference, pages 153–159, University of Texas, 21. - 23. June 1990. Morgan Kaufmann Publishers, Inc. ga:Bonelli90.

74


[778] Royce O. Bowden. Genetic algorithm based machine learning applied to the dynamic routing of discrete parts. PhD thesis, Mississippi State University, 1992. * DAI Dec. 93 ga:BowdenThesis. [779] C. Carrick and K. MacLeod. An evaluation of genetic algorithm solutions in optimization and machine learning. In ?, editor, 21st Annual Conference Canadian Association for Information Science CAIS/ACSI’93, pages 224–231, Antigonish, NS, Canada, 12.-14. July 1993. Canadian Association for Information Science, Toronto. * CCA 45486/94 ga:Carrick93a. [780] Fran¸cois E. Cellier. In Continuous System Modeling, chapter 14. Artificial neural networks and genetic algorithms, pages 623–701. Springer-Verlag, Berlin, 1991. ga:Cellier91a. [781] C. H. Chu and C. R. Chow. A genetic algorithm approach to supervised learning for multilayered networks. In Proceedings of the World Congress on Neural Networks - WCNN ’93, volume IV, pages 744–747, Portland, OR, 11.-15. July 1993. Lawrence Erlbaum Ass., Inc., Hillsdale, NJ. ga:Chow93a. [782] Helen G. Cobb. Is the genetic algorithm a cooperative learner. In Whitley [948], pages 277–296. ga:Cobb92a. [783] Diane J. Cook. Using analytic and genetic methods to learn plans for mobile robots. In ?, editor, Proceedings of the SPIE’s OE/Aerospace and Remote Sensing Symposium (Machine Vision and Robotics), volume SPIE1964, pages ?–?, Orlando, FL, 12. -16. April 1993. The International Society for Optical Engineering, Bellingham, WA. †GAdigest V. 9 N. 48 ga:DJCook93a. [784] Lawrence Davis. Hybrid genetic algorithms for machine learning. In IEE Colloquium on Machine Learning, volume Digest No. 117, pages 9/1–9/3, London, 28. June 1990. IEE, London. †Fogel/bib ga:Davis90c. [785] Lawrence Davis, Stewart W. Wilson, and David Orvosh. Temporary memory for examples can speed learning in a simple adaptive system. In Roitblat et al. [949], pages 313–320. ga:Davis93b. [786] William M. Spears and Kenneth A. De Jong. Using genetic algorithms for supervised concept learning. In Apostolos Dollas and Nikolaos G. Bourbakis, editors, Proceedings of the 1990 IEEE International Conference on Tools with Artificial Intelligence TAI’90, pages 335–341, Herndon, VA, 6.-9. November 1990. IEEE Computer Society Press, Los Alamitos, CA. ga:DeJong90e. [787] Kenneth A. De Jong and William M. Spears. Learning concept classification rules using genetic algorithms. In IJCAI-91 Proceedings of the Twelfth International Conference on Artificial Intelligence, volume 2, pages 651–656, Sydney, 24. - 30. August 1991. Morgan Kaufmann Publishers. ga:DeJong91a. [788] Haleh Vafaie and Kenneth A. De Jong. Genetic algorithms as a tool for feature selection in machine learning. In Proceedings of the 1992 IEEE International Conference on Tools with Artificial Intelligence TAI’92, pages 200–203, Arlington, VA, 10.-13. November 1992. IEEE Computer Society Press, Los Alamitos, CA. ga:DeJong92d. [789] L. De Raedt, B. Vandersmissen, M. Denecker, and M. Bruynooghe. A hybrid approach to learning and its knowledge representation. In Teuvo Kohonen and Fran¸coise Fogelman-Soulie, editors, Cognitiva 90 At the Crossroads of Artificial Intelligence, Cognitive Science, and Neuroscience, Proceedings of the Third COGNITIVA Symposium, pages 109–116, Madrid, 20.-23. November 1990. North-Holland. †ACM/92 ga:DeRaedt90. [790] Marco Dorigo. Using transputers to increase speed and flexibility of genetics-based machine learning systems. Microprocessing and Microprogramming EURO-Micro Journal, 34(1-5):147–152, 1992. ga:Dorigo92a. [791] Marco Dorigo and Marco Colombetti. Robot shaping: Developing situated agents through learning. Technical Report TR-92-040, International Computer Science Institute (ICSI), Berkeley, CA, 1992. (available via anonymous ftp site icsi.berkeley.edu directory /pub/techreports/1992 file tr-92-040.ps.Z) ga:Dorigo92b. [792] Marco Colombetti and Marco Dorigo. Training agents to perform sequential behavior. Technical Report Technical Report No. 93-023, International Computer Science Institute (ICSI), Berkeley, CA, 1993. (available via anonymous ftp site icsi.berkeley.edu directory /pub/techreports/1993 file tr-93-023.ps.Z) ga:Dorigo93e. [793] Marco Dorigo. Optimization, Learning and Natural Algorithms. PhD thesis, Politechnico di Milano, 1992. (in Italian) †Fogel/bib ga:DorigoThesis. [794] Peter Dudey. An evolutionary program to mimic language acquisition GALE (Genetic Acquisition of a Language of Exchange), 1985. (a program manual) ga:Dudey93a. [795] Edward O. Gordon. Discovery learning in autonomous agents using genetic algorithms. Master’s thesis, Air Force Institute of Technology, Wright-Patterson AFB, School of Engineering, 1993. * N94-25998 ga:EOGordonMSThesis.

Bibliography

75

[796] Arnold C. Englander. Machine learning of visual recognition using genetic algorithms. In Grefenstette [906], pages 197–201. ga:Englander85. [797] J. Doyne Farmer, Norman H. Packard, and Alan S. Perelson. The immune system, adaptation, and machine learning. Physica D, 22(?):187–204, 1986. † ga:Farmer86b. [798] David B. Fogel. System Indentification Through Simulated Evolution: A Machine Learning Approach to Modeling. Ginn Press, Needham Heights, MA, 1991. ga:Fogel91book. [799] Toshio Fukuda, Tadashi Kohno, and Takanori Shibata. Dynamic memory by recurrent neural network and its learning by genetic algorithm. In Proceedings of the 32nd IEEE Conference on Decision and Control, volume 3, pages 2815–2820, San Antonio, TX, 15.-17. December 1993. IEEE Control Systems Society. ga:Fukuda93t. [800] Peter M. Todd and Geoffrey F. Miller. Exploring adaptive agency II: Simulating the evolution of associative learning. In Meyer and Wilson [928], pages 306–315. ga:GFMiller91b. [801] Gerhard Weiß. Action selection and learning in multi-agent environments. In Roitblat et al. [949], pages 502–510. ga:GWeiss93a. [802] Andrew McGilvary Gillies. Machine learning procedures for generating image domain feature detectors, 1985. (U. S. patent no. 4,821,333. Issued Apr. 11 1989) †GAdigest v8 n10/Park ga:Gillies89a. [803] Andrew McGilvary Gillies. Machine learning procedures for generating image domain feature detectors. PhD thesis, University of Michigan, 1985. * DAI 46/7 ga:GilliesThesis. [804] M. Botta, A. Giordana, and L. Saitta. Learning fuzzy concept definition. In ?, editor, Proceedings of the Second IEEE International Conference on Fuzzy Systems, pages 18–22, San Francisco, CA, March 28. -April 1. 1993. IEEE New York. * EI 103911/93 ga:Giordana93a. [805] A. Giordana and L. Saitta. REGAL: an integrated system for learning relations using genetic algorithms. In ?, editor, Proceedings of the Second International Workshop on Multistrategy Learning (MSL-93), pages 234–249, Harpers Ferry, WV, 26.-29. May 1993. George Mason University, Fairfax, VA. * CCA 17022/95 ga:Giordana93b. [806] David E. Goldberg. Genetic algorithms and rules learning in dynamic system control. In Grefenstette [906], pages 8–15. ga:Goldberg85b. [807] David E. Goldberg. Computer-aided gas pipeline operation using genetic algorithms and rule learning. Part II: Rule learning control of a pipeline under normal and abnormal conditions. Engineering with Computers, 3(?):47–58, 1987. ga:Goldberg87g. [808] David E. Goldberg. Genetics-based machine learning: Whence it came, where it’s going. In M. S. Elzas, T. I. Oren, and B. P. Zeigler, editors, Modelling and simulation methodology, Knowledge Systems’ Paradigms, pages 285–300, Tucson, AZ, 19.-23. January 1989. North-Holland, Amsterdam. ga:Goldberg89g. [809] David Perry Greene and Stephen F. Smith. A genetic system for learning models of consumer choice. In Grefenstette [908], pages 217–223. ga:Greene87. [810] John J. Grefenstette. Credit assignment in genetic learning systems. In Proceedings AAAI-88 Seventh National Conference on Artificial Intelligence, volume 2, pages 596–600, St. Paul, Minnesota, 21. - 26. August 1988. ga:Grefenstette88a. [811] John J. Grefenstette. Competition-based learning for reactive systems. In 1990 DARPA Workshop on Innovative Approaches to Planning, Scheduling and Control, pages 384–353, ?, ? 1990. ? (Also AIC Report No. AIC-90-008) † ga:Grefenstette90c. [812] Alan C. Schultz and John J. Grefenstette. Improving tactical plans with genetic algorithms. In Apostolos Dollas and Nikolaos G. Bourbakis, editors, Proceedings of the 1990 IEEE International Conference on Tools with Artificial Intelligence TAI’90, pages 328–334, Herndon, VA, 6.-9. November 1990. IEEE Computer Society Press, Los Alamitos, CA. ga:Grefenstette90d. [813] John J. Grefenstette. Lamarckian learning in multi-agent environments. In Belew and Booker [926], pages 303–310. ga:Grefenstette91c. [814] John J. Grefenstette. Learning decision strategies with genetic algorithms. In K. P. Jantke, editor, Analogical and Inductive Inference, Proceedings of the International Workshop on Analogical and Inductive Inference (AII 92), volume Lecture Notes in Artificial Intelligence, Vol. 642, pages 35–50, Dagstuhl Castle (Germany), 5.-9. October 1992. Springer Verlag, Berlin. * P56365 CCA 3583/95 ga:Grefenstette92a. [815] Alan C. Schultz and John J. Grefestette. Using a genetic algorithm to learn behaviors for autonomous vehicles. In ?, editor, Proceedings of the AIAA Guidance, Navigation and Control Conference, volume ?, pages 739–749, Hilton Head, SC, 10.-12. August 1992. AIAA. ga:Grefenstette92e.

76


[816] John J. Grefenstette. Genetic algorithms and machine learning. In L. Pitt, editor, Proceedings of the Sixth Annual ACM Conference, pages 3–4, Santa Cruz, CA, 26.-28. July 1993. ACM Press, New York. †ACM/93 ga:Grefenstette93f. [817] Hans Ros. Some results on Boolean concept learning by genetic algorithms. In Schaffer [907], pages 28–33. ga:HRos89. [818] Raed Abdullah Abu Zitar and Mohamad H. Hassoun. Regulator control via genetic search assisted reinforcement learning. In Forrest [905], pages 254–262. ga:Hassoun93d. [819] J. L. Castro, M. Delgado, and Francisco Herrera. A learning method of fuzzy reasoning by genetic algorithms. In ?, editor, Proceedings of the First European Congress on Fuzzy and Intelligent Technologies (EUFIT’93), volume 2, pages 804–809, Aachen (Germany), ? 1993. ? (available via anonymous ftp site decsai.ugr.es directory pub/arai/tech rep/ga-fl file eufit93.ps.Z) ga:Herrera93b. [820] Kenneth J. Hintz. Procedure learning using a variable-dimension solution space. In Schaffer [907], pages 237–242. ga:Hintz89. [821] John H. Holland. A mathematical framework for studying learning in classifier systems. Technical Report Research Memo RIS-25r, The Rowland Institute for Science, Cambridge, MA, 1985. † ga:Holland85b. [822] John H. Holland and A. W. Burks. Adaptive computing system cabable of learning and discovery, 1985. (U. S. Patent no. 4,697,242. Issued Sep. 29 1987) † ga:Holland85c. [823] John H. Holland, Keith J. Holyoak, Richard E. Nisbett, and Paul R. Thagard. Induction: Processes of Inference, Learning, and Discovery. MIT Press, Cambridge, MA, 1986. †[950] ga:Holland86book. [824] Vasant Honavar. Toward learning systems that integrate different strategies and representations. Technical report ISU-CS-TR 93-22, Iowa State University, Department of Computer Science, Ames, IA, 1993. (available via anonymous ftp site ftp.cs.iastate.edu directory /pub/techreports/AI-reports file TR93-22.ps.Z) ga:Honavar93a. [825] Shih-Lin Hung. Neural network and genetic learning algorithms for computer-aided design and pattern recognition. PhD thesis, The Ohio State University, 1992. * DAI 53/11 ga:HungThesis. [826] Johannes P. Ros. Learning Boolean functions with genetic algorithms: A PAC analysis. In Whitley [948], pages 257–276. ga:JPRos92a. [827] Johannes P. Ros. Learning Boolean functions with genetic algorithms: A PAC analysis. PhD thesis, University of Pittsburgh, 1992. * DAI 53/9 ga:JPRosThesis. [828] J. Reed, R. Toombs, and N. A. Barricelli. Simulation of biological evolution and machine learning. Journal of Theoretical Biology, 17(?):319–342, 1967. † ga:JReed67. [829] Cezary Z. Janikow. Inductive learning of decision rules from attribute-based examples: A knowledge-intensive genetic algorithm approach. PhD thesis, University of North Carolina at Chapel Hill, 1991. †ACM/92 Michalewicz92book ga:JanikowThesis. [830] J. N. R. Jeffers. Rule induction methods in forestry research. AI Applications, 5(2):37–44, ? 1991. †ga94aKettunen ga:Jeffers91a. [831] Keiji Suzuki, H. Sakanashi, and Yukinori Kakazu. Genetic algorithms exploiting internal environment representation with schema extraction. In ?, editor, Proceedings of the Fifth Workshop on Neural Networks: Academic/Industrial/NASA/Defence, volume SPIE-2204, pages 125–132, San Francisco, CA, 7.-10. November 1993. The International Society for Optical Engineering. * CCA 75938/94 P59914/94 ga:Kakazu93c. [832] Sadayoshi Mikami and Yukinori Kakazu. Extended stochastic reinforcement learning for the acquisition of cooperative motion plans for dynamically constrained agents. In 1993, International Conference on Systems, Man and Cybernetics, volume 4, pages 257–262, Le Touquet (France), 17.-20. October 1993. IEEE, New York. * EI M064773/94 ga:Kakazu93d. [833] R. R. Kampfner. Computational modeling of evolutionary learning. PhD thesis, University of Michigan, Ann Arbor, 1981. (University Microfilms No. 81-25143) † ga:Kampfner81. [834] Charles L. Karr and David E. Goldberg. Genetic algorithms in mineral processing and machine learning. In Proceedings of the Artificial Intelligence in Minerals and Materials Technology Conference, pages 127– 141, The University of Alabama, 20.-21. October 1987. U. S. Department of the Interior Bureau of Mines. ga:Karr87a. [835] A. Kirlik. Acquisition and production of skilled behavior in dynamic decision-making tasks. Goverment report N92-31341/0, Georgia Institute of Technology, 1992. * CA 3822 Vol. 37 No. 5/6 ga:Kirlik92a.

Bibliography

77

[836] Hiroaki Kitano, Stephen F. Smith, and Tetsuya Higuchi. GA-1: A parallel associative memory processor for rule learning with genetic algorithms. In Belew and Booker [926], pages 311–317. ga:Kitano91. [837] Venkataramana Kommu, Irith Pomeranz, and T. Abdelrahman. A genetic learning strategy in constrained search spaces. In Proceedings of the Twenty-Fifth International Conference on System Sciences, volume 3, pages 26–35, Kauai,HI, 7.-10. January 1991. IEEE Computer Society Press, Los Alamitos, CA. * CCA 63014/92 ga:Kommu91. [838] John R. Koza. A hierarchical approach to learning the Boolean multiplexer function. In Rawlins [927], pages 171–192. ga:Koza91e. [839] John R. Koza, Martin A. Keane, and James P. Rice. Performance improvement of machine learning via automatic discovery of facilitating functions as applied to a problem of symbolic system identification. In 1993 IEEE International Conference on Neural Networks, volume I, pages 191–198, San Francisco, CA, 28. March - 1. April 1993. IEEE. ga:Koza93a. [840] Lingyan Shu. The impact of data structures on the performance of genetic-algorithm-based learning. PhD thesis, University of Alberta, Canada, 1992. * DAI 54/3 ga:LShuThesis. [841] C.-E. Liedtke, T. Schnier, and A. Bloemer. Automated learning of rules using genetic operators. In ?, editor, Proceedings of the 5th International Conference on Computer Analysis of Images and Patterns, pages 327–334, Budapest (Hungary), 13.-15. September 1993. Springer-Verlag, Berlin. * CCA 86430/94 EEA 95681/94 ga:Liedtke93a. [842] Gunar E. Liepins and M. R. Hilliard. Representational issues in machine learning. In Proceedings of the International Symposium on Methodologies for Intelligent Systems, Colloquia Program, pages 3–13, ?, ? 1986. ? (ORNL-6362) † ga:Liepins86b. [843] M. R. Hilliard and Gunar E. Liepins. Representational issues in machine learning. In Proceedings of the International Symposium on Methodologies for Intelligent Systems, pages 1–15, ?, ? 1987. ? † ga:Liepins87c. [844] M. R. Hilliard, Gunar E. Liepins, and Mark R. Palmer. Machine learning applications to job shop scheduling. In M. Ali, editor, Proceedings: The First International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA/AIE-88), pages 728–737, University of Tennessee, Space Institute, Tullahoma, TN, 1.-3. June 1988. ACM, New York. † ga:Liepins88b. [845] M. R. Hilliard, Gunar E. Liepins, and Mark R. Palmer. Discovering and refining algorithms through machine learning. In D. E. Brown and C. C.White, editors, Operations research and artificial intelligence: The integration of problem solving strategies, pages 59–78. Kluwer Academic Publishers, Boston, MA, 1990. † ga:Liepins90b. [846] Mitchell A. Potter. A genetic cascade-correlation learning algorithm. In J. David Schaffer and Darrell Whitley, editors, COGANN-92, International Workshop on Combinations of Genetic Algorithms and Neural Networks, pages 123–133, Baltimore, MD, 6. June 1992. IEEE Computer Society Press, Los Alamitos, CA. * CCA 58171/93 ga:MAPotter92a. [847] Bruce J. MacLennan. Synthetic ethology: An approach to the study of communication. In Langton et al. [947], pages 631–658. ga:MacLennan92a. [848] Michael de la Maza. A seagul visits the race track. In Schaffer [907], pages 208–212. ga:Maza89. [849] Alastair D. McAulay and Jae Chan Oh. Inductive character learning and classification with genetic algorithms. In IEEE International Conference on Systems Engineering, pages 363–366, Fairborn, OH, 1.-3. August 1991. IEEE, New York. †P48468 ga:McAulay91a. [850] J. Wnek, Jayshree Sarma, A. A. Wahab, and R. S. Michalski. Comparing learning paradigms via diagrammatic visualization: A case study in single concept learning using symbolic, neural net and genetic algorithm methods. Technical report, George Mason University, Computer Science Department, 1990. †[951] ga:Michalski90b. [851] Heinz M¨ uhlenbein. Parallel genetic algorithms and neural networks as learning machines. In D. J. Evans, G. R. Joubert, and H. Liddell, editors, Proceedings of the International Conference Parallel Computing ’91, pages 91–103, London, 3.-6. September 1991. North-Holland, Amsterdam. †Fogel/bib ga:Muhlenbein91f. [852] Marco Muselli and Sandro Ridella. Supervised learning using a genetic algorithm. In INNC 90 - International Neural Network Conference, volume 2, page ?, Paris, 9.-13. July 1990. Kluwer, Dordrecht, Netherlands. †Fogel/bib ga:Muselli90. [853] Michael O. Odetayo. On Genetic Algorithms in Machine Learning and Optimization. PhD thesis, University of Strathclyde, Department of Computer Science, 1990. †[952] ga:OdetayoThesis.

78


[854] Anon. How machines live and learn. Personal Computer World, 16(6):483–484, 1993. ga:PCW93/6b. [855] Geoffrey F. Miller and Peter M. Todd. Exploring adaptive agency I: Theory and methods for simulating the evolution of learning. In D. S. Touretzky, J. L. Elman, T. J. Sejnowski, and G. E. Hinton, editors, Proceedings of the 1990 Connectionist Model Summer School, volume ?, pages 65–80, ?, ? 1990. Morgan Kaufmann, San Mateo, CA. †Todd ga:PMTodd90a. [856] Peter M. Todd. Parental guidance suggested: How parental imprinting evolves through sexual selection as an adaptive learning mechanism. Adaptive Behavior, 2(1):5–47, 1993. †Todd ga:PMTodd93c. [857] Peter M. Todd. The evolution of learning: Simulating the interaction of adaptive processes. PhD thesis, Stanford University, Psychology Department, 1992. †Todd ga:PMToddThesis. [858] Norman H. Packard. A genetic learning algorithm for the analysis of complex data. Complex Systems, 4(5):543–572, October 1990. ga:Packard90. [859] Jan Paredis. The evolution of behaviour: Some experiments. In Meyer and Wilson [928], pages 419–426. ga:Paredis91a. [860] Domenico Parisi, Stefano Nolfi, and Federico Cecconi. Learning, behavior, and evolution. In Varela and Bourgine [932], pages 207–216. ga:Parisi91a. [861] Marcello Pelillo, Fabio Abbattista, and Angelo Maffione. Evolutionary learning for relaxation labeling processes. In ?, editor, Advances in Artificial Intelligence. Proceedings of the Third Congress of the Italian Association for Artificial Intelligence, AI*IA’93, pages 230–241, Torino (Italy), 26.-28. October 1993. Springer-Verlag, Berlin. †CCA 86395/98 ga:Pelillo93a. [862] Ulrich Dorndorf and Erwin Pesch. Evolution based learning in a job shop scheduling environment. Technical Report RM 92-019, Limburg University, Faculty of Economics, 1992. ga:Pesch92a. [863] Robert E. Marks. Breeding hybrid strategies: Optimal behavior for oligopolists. In Schaffer [907], pages 198–207. ga:REMarks89. [864] Robert E. Marks. Breeding hybrid strategies: Optimal behavior for oligopolists. Journal of Evolutionary Economics, 2:17–38, 1992. †[?] Marks/News ga:REMarks92a. [865] Aaron L. Ranson, Aurali B. Franco, and Margarita G. Chavez. Genetic algorithm like learning rule for neural networks. In 1993, International Conference on Systems, Man and Cybernetics, volume 4, pages 137–142, Le Touquet (France), 17.-20. October 1993. IEEE, New York. ga:Ranson93a. [866] Larry A. Rendell. A doubly layered, genetic penetrance learning system. In Proceedings of the National Conference on Artificial Intelligence, pages 343–347, ?, ? 1983. ? † ga:Rendell83a. [867] Larry A. Rendell. Genetic plans and the probabilistic learning system: synthesis and results. In Grefenstette [906], pages 60–73. ga:Rendell85. [868] Robert G. Reynolds. The control of genetic algorithms using version spaces. In Apostolos Dollas and Nikolaos G. Bourbakis, editors, Proceedings of the 1990 IEEE International Conference on Tools with Artificial Intelligence TAI’90, pages 342–348, Herndon, VA, 6.-9. November 1990. IEEE Computer Society Press, Los Alamitos, CA. ga:Reynolds90. [869] Robert G. Reynolds, Jonathan I. Maletic, and Shan-Ping Chang. The use of version space controlled genetic algorithms to solve the Boole problem. In Proceedings of the 1991 IEEE International Conference on Tools with Artificial Intelligence TAI’91, pages 14–21, San Jose, CA, 10. - 13. November 1991. IEEE Computer Society Press, Los Alamitos, CA. ga:Reynolds91a. [870] William Sverdlik, Robert G. Reynolds, and Elena Zannoni. Hybal: a self tutoring algorithm for concept learning in highly autonomous systems. In Proceedings of the 3rd Conference on Artificial Intelligence, Simulation and Planning in High Autonomous Systems, page ?, Perth, Australia, 8. - 10. July 1992. IEEE Computer Society Press, Los Alamitos, CA. ga:Reynolds92a. [871] William Sverdlik and Robert G. Reynolds. Dynamic version spaces in machine learning. In Proceedings of the 1992 IEEE International Conference on Tools with Artificial Intelligence TAI’92, pages 308–315, Arlington, VA, 10.- 13. November 1992. IEEE Computer Society Press, Los Alamitos, CA. ga:Reynolds92b. [872] Louis A. Tamburino and Mateen M. Rizki. Applications of hybrid learning to automated system design. In Proceedings of the AI, Simulation and Planning in High Autonomous Systems Conference 1990, pages 176–183, Tuczon, AZ, 26.-27. March 1990. IEEE, Piscataway, NJ. * www /IEEE ga:Rizki90a. [873] Stephen F. Smith. Flexible learning of problem solving heuristics through adaptive search. In Proceedings of the 8th Joint Conference on Artificial Intelligence, pages 422–425, ?, ? 1983. Morgan Kaufman, Los Altos, CA. †Michalewicz92book ga:SFSmith83a.

Bibliography

79

[874] Stephen F. Smith. A learning system based on genetic adaptive algorithms. PhD thesis, University of Pittsburgh, 1980. (University Microfilms No. 81-12638) †Michalewicz92book ga:SFSmithThesis. [875] Stephen J. Smith and Stewart W. Wilson. Rosetta: Towards a model of learning problems. In Schaffer [907], pages 347–350. ga:SJSmith89. [876] Sandip Sen and Janani Janakiraman. Learning to construct puhdown automata for accepting deterministic context-free languages. In Gautam Biswas, editor, Applications of Artificial Intelligence X: KnowledgeBased Systems, volume SPIE-1707, pages 207–213, Orlando, FL, 22. - 24. April 1992. The International Society for Optical Engineering. * EI A083789/92 ga:SSen92. [877] Liisa Saarenmaa. Induktiivinen oppiminen mets¨ anviljelyn tietokannan tulkinnassa [Inductive learning in interpretation of databases of forest cultivation]. PhD thesis, University of Helsinki, Department of Forest Ecology, 1992. ga:Saarenmaa92thesis. [878] Timo Honkela and Tuomas Sandholm. In Eero Hyv¨ onen, Ilkka Karanta, and Markku Syrj¨ anen, editors, Teko¨ alyn ensyklopedia [Ensyclopedy of artificial intelligence], chapter 5.3 Koneoppiminen [Machine learning], pages 244–255. Gaudeamus, H¨ ameenlinna, 1993. (in Finnish) ga:Sandholm93a. [879] II Adrian V. Sannier and Erik D. Goodman. Genetic learning procedures in distributed environments. In Grefenstette [908], pages 162–169. ga:Sannier87. [880] II Adrian V. Sannier. A computational theory of learning in distributed systems. PhD thesis, Michigan State University, 1988. ga:Sannier88thesis. [881] J. David Schaffer and John J. Grefenstette. Multi-objective learning via genetic algorithms. In A. Joshi, editor, IJCAI 85 Proceedings of the Ninth International Joint Conference on Artificial Intelligence, volume 1, pages 593–595, Los Angeles, CA, 18. - 23. August 1985. †P38581 ga:Schaffer85a. [882] J. David Schaffer. Some experiments in machine learning using vector evaluated genetic algorithms. PhD thesis, Vanderbilt University, Nashville, TN, 1984. (University Microfilms No. 85-22492) † ga:SchafferThesis. [883] Alan C. Schultz. Adapting the evaluation space to improve global learning. In Belew and Booker [926], pages 158–164. ga:Schultz91a. [884] Hans-Paul Schwefel. Evolutionary learning optimum–seeking on parallel computer architectures. In A. Sydow, S. G. Tzafestas, and R. Vichnevetsky, editors, Systems Analysis and Simulation 1988, volume 1, pages 217–225. Akademie-Verlag, Berlin, 1988. ga:Schwefel88c. [885] Hans-Paul Schwefel. Simulation evolution¨ aren Lernprozesse. In D. P. F. M¨ oller, editor, Erwin-RieschWorkshop on Systems Analysis of Biomedical Processen, pages 17–30, Bad M¨ unster am Stein - Ebernburg (Germany), 7.-9. April 1988. German Society for Computer Sciences (GAI) - Technical Committee for Simulation (ASIM), Vieweg, Braunschweig. ga:Schwefel89c. [886] Hans-Paul Schwefel. Collective learning by artificial evolution. In P. Bock, F. J. Radermacher, and M. M. Richter, editors, Proceedings of the FAW Workshop on Adaptive Learning, pages 177–182, Schloß Reisensburg, G¨ unzburg (Germany), 16.-21. July 1989. Research Institute for Applied Knowledge Engineering (FAW), Ulm, Research Institute for Applied Knowledge Engineering (FAW). ga:Schwefel89d. [887] Hans-Paul Schwefel. Understanding evolution as a collective strategy for groping in the dark. Number 565 in Lecture Notes in Artificial Intelligence, pages 388–397. Springer-Verlag, Berlin, Neubiberg (Germany) and Wildbad Kreuth (Germany), 10.-11. March and 24.-28. July 1989 1991. ga:Schwefel91b. [888] Hans-Paul Schwefel. Imitating evolution: Collective two-level learning processes. In U. Witt, editor, Explaining Process and Change – Approaches to Evolutionary Economics, pages 49–63. The University of Michigan Press, Ann Arbor, MI, 1992. ga:Schwefel92a. [889] Hans-Paul Schwefel. Natural evolution and collective optimum seeking. In A. Sydow, editor, Computational Systems Analysis – Topics and Trends, pages 5–14. North-Holland, Amsterdam, 1992. ga:Schwefel92b. [890] Alberto Maria Segre. Applications of machine learning. IEEE Expert, 7(3):30–34, 1992. ga:Segre92. [891] Riyaz Sikora. Learning control strategies for chemical processes, A distributed approach. IEEE Expert, 7(3):35–43, 1992. ga:Sikora92. [892] William M. Spears and Diana F. Gordon. Adaptive strategy selection for concept learning. In Ryszard S. Michalski and Gheorghe Tecuci, editors, Proceedings of the First International Workshop on Multistrategy Learning (MSL-91), pages 231–246, Harpers Ferry, 7. - 9. November 1991. ga:Spears91c. [893] Joachim Stender and Tom Addis. Symbols versus neurons? chapter Using the genetic algorithm to adapt intelligent systems, page ? IOS Press, Amsterdam, 1990. † ga:Stender90a.

80


[894] R. Keesing and David G. Stork. Evolution and learning in neural networks: The number and distribution on learning trials affect the rate of evolution. In Richard P. Lippmann, J. E. Moody, and D. S. Touretzky, editors, Advances in Neural Information Processing Systems 3. Proceedings of the 1990 Conference (NIPS3), page ?, Denver, CO, 26.-29. November 1990. Morgan Kaufmann, Palo Alto, CA. †[953] ga:Stork90a. [895] Suchen Su and Kiichi Tsuchiya. Learning of a maze using a genetic algorithm. In Proceedings of the 19th Annual Conference of IEEE Industrial Electronic Society (IECON’93), volume 1, pages 376–379, Maui, HI, November 1993. IEEE Press, New York. ga:Su93a. [896] Okamoto J. Sugimoto and S. Hosokawa. A learning method in neural network using genetic algorithm. In ?, editor, Proceedings of Kansai-section Joint Convention of Institute of Electrical Engineering, volume G235 G8-8, page ?, ?, ? 1991. ? †[392] ga:Sugimoto91a. [897] James Zhen Tu. Genetic algorithms in machine learning and optimization. PhD thesis, University of Cincinnati, 1992. * DAI 53/6 ga:TuThesis. [898] Gilles Venturini. Characterizing the adaptation abilities of a class of genetic based machine learning algorithms. In Varela and Bourgine [932], pages 302–309. ga:Venturini91a. [899] Kwong Sak Leung, Yee Leung, Leo So, and Kim Fai Yam. Rule learning in expert systems using genetic algorithm: 1, concepts. In Proceedings of the 2nd International Conference on Fuzzy Logic and Neural Networks (IIZUKA’92), volume 1, pages 201–204, Iizuka (Japan), 17.-22. July 1992. Fuzzy Logic Systems Institute. ga:Yam92a. [900] Kwong Sak Leung, Yee Leung, Leo So, and Kim Fai Yam. Rule learning in expert systems using genetic algorithm: 2, empirical studies. In Proceedings of the 2nd International Conference on Fuzzy Logic and Neural Networks (IIZUKA’92), volume 1, pages 205–208, Iizuka (Japan), 17.-22. July 1992. Fuzzy Logic Systems Institute. ga:Yam92b. [901] Hugo de Garis. ’compo’ conceptual clustering with connectionist competitive learning. In Proceedings of the First IEE International Conference on Artificial Neural Networks, pages 226–232, London, 16.-18. October 1989. IEE. ga:deGaris89a. [902] Hugo de Garis. Incremental genetic programming, multistrategy learning in neural nets. In ?, editor, Proceedings of the Multistrategy Learning Workshop (MSL’93), volume ?, page ?, West Virginia, May 1993. ? †de Garis ga:deGaris93k. [903] Torbj¨ orn E. M. Nordling, Janne Koljonen, Josefina Nystr¨ om, Ida Bodén, Britta Lindholm-Sethson, Paul Geladi, and Jarmo T. Alander. Wavelength selection by genetic algorithms in near infrared spectra for melanoma diagnosis. In ?, editor, Proceedings of the 3rd European Medical and Biological Engineering Conference (EMBEC’05), volume 11, page ? [904] Stephanie Forrest, editor. Emergent Computation: Self-Organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks, Cambridge, MA, 1991. MIT Press / North-Holland. (also as Physica D, Vol. 42) ga:Forrest91book. [905] Stephanie Forrest, editor. Proceedings of the Fifth International Conference on Genetic Algorithms, UrbanaChampaign, IL, 17.-21. July 1993. Morgan Kaufmann, San Mateo, CA. ga:GA5. [906] John J. Grefenstette, editor. Proceedings of the First International Conference on Genetic Algorithms and Their Applications, Pittsburgh, PA, 24. - 26. July 1985. Lawrence Erlbaum Associates: Hillsdale, New Jersey. ga:GA1. [907] J. David Schaffer, editor. Proceedings of the Third International Conference on Genetic Algorithms, Georg Mason University, 4.-7. June 1989. Morgan Kaufmann Publishers, Inc. ga:GA3. [908] John J. Grefenstette, editor. Genetic Algorithms and their Applications: Proceedings of the Second International Conference on Genetic Algorithms and Their Applications, MIT, Cambridge, MA, 28. - 31. July 1987. Lawrence Erlbaum Associates: Hillsdale, New Jersey. ga:GA2. [909] Proceedings of the First IEEE Conference on Evolutionary Computation, Orlando, FL, 27.-29. June 1994. IEEE, New York, NY. ga94ICCIEC. [910] Yuval Davidor, Hans-Paul Schwefel, and Reinhard Manner, editors. Parallel Problem Solving from Nature – PPSN III, volume 866 of Lecture Notes in Computer Science, Jerusalem (Israel), 9.-14. October 1994. Springer-Verlag, Berlin. † ga94PPSN3. [911] Proceedings of the Second European Congress on Intelligent Techniques and Soft Computing (EUFIT’94), Aachen (Germany), 20.-23. September 1994. ELITE-Foundation. ga94EUFIT. [912] Proceedings of ICCI94/Fuzzy Systems, Orlando, FL, 26. June - 2. July 1994. IEEE, New York, NY. ga94ICCIFuzzy.

†

Bibliography

81

[913] A. V. Sebald and Lawrence J. Fogel, editors. Proceedings of the Fourth Annual Conference on Evolutionary Programming (EP94), San Diego, CA, 24.-26. February 1994. World Scientific, Singapore. †Fogel ga94EP. [914] Terrence C. Fogarty, editor. Evolutionary Computing, AISB Workshop Selected Papers, volume 865 of Lecture Notes in Computer Science, ?, ? 1994. Springer-Verlag, Berlin. †Fogarty ga94Springer865. [915] Larry J. Eshelman, editor. Proceedings of the Sixth International Conference on Genetic Algorithms, Pittsburgh, PA, 15.-19. July 1995. ? †prog ga95ICGA. [916] D. W. Pearson, N. C. Steele, and R. F. Albrecht, editors. Artificial Neural Nets and Genetic Algorithms, Alès (France), 19.-21. April 1995. Springer-Verlag, Wien New York. ga95ICANNGA. [917] Lance D. Chambers, editor. Practical Handbook of Genetic Algorithms, volume 2, Applications. CRC Press, Boca Raton, FL, 1995. ga95CRC2. [918] Proceedings of the First IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, Sheffield (UK), 12.-14. September 1995. IEEE. †conf. prog. ga95Sheffield. [919] Proceedings of the Second IEEE Conference on Evolutionary Computation, Perth (Australia), November 1995. IEEE, New York, NY. ga95ICEC. [920] G. Winter, J. Périaux, M. Gal´ an, and P. Cuesta, editors. Genetic Algorithms in Engineering and Computer Science (EUROGEN95), Las Palmas (Spain), December 1995. John Wiley & Sons, New York. ga95LasPalmas. [921] Hans-Michael Voigt, Werner Ebeling, Ingo Rechenberg, and Hans-Paul Schwefel, editors. Parallel Problem Solving from Nature – PPSN IV, volume 1141 of Lecture Notes in Computer Science, Berlin (Germany), 22.-26. September 1996. Springer-Verlag, Berlin. ga96PPSN4. [922] Ian Parmee and M. J. Denham, editors. Adaptive Computing in Engineering Design and Control ’96 (ACEDC’96), 2nd International Conference of the Integration of Genetic Algorithms and Neural Network Computing and Related Adaptive Techniques with Current Engineering Practice, Plymouth (UK), 26.-28. March 1996. ? (to appear) †conf.prog. ga96Plymouth. [923] John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors. Proceedings of the GP-96 Conference, Stanford, CA, 28.-31. July 1996. MIT Press, Cambridge, MA. †prog ga96GP. [924] John R. Koza, Kalyanmoy Deb, Marco Dorico, David B. Fogel, Max Garson, Hitoshi Iba, and Rick L. Riolo, editors. Genetic Programming 1997: Proceedings of the Second Annual Conference, Stanford, CA, 13.-16. July 1997. Morgan Kaufmann, San Francisco, CA. †prog ga97GP. [925] Pavel Oˇsmera, editor. Proceedings of the 4th International Mendel Conference on Genetic Algorithms, Optimization problems, Fuzzy Logic, Neural networks, Rough Sets (MENDEL’98), Brno (Czech Republic), 24.-26. June 1998. Technical University of Brno. ga98Brno. [926] Richard K. Belew and Lashon B. Booker, editors. Proceedings of the Fourth International Conference on Genetic Algorithms, San Diego, 13.-16. July 1991. Morgan Kaufmann Publishers. ga:GA4. [927] Gregory J. E. Rawlins, editor. Foundations of Genetic Algorithms, Indiana University, 15.-18. July 1990 1991. Morgan Kaufmann: San Mateo, CA. ga:FOGA1. [928] Jean-Arcady Meyer and Stewart W. Wilson, editors. Proceedings of the First International Conference on Simulation of Adaptive Behavior: From animals to animats, Paris, 24.-28. September 1991. A Bradford Book, MIT Press, Cambridge, MA. ga:SAB90. [929] R. F. Albrecht, C. R. Reeves, and N. C. Steele, editors. Artificial Neural Nets and Genetic Algorithms, Innsbruck, Austria, 13. -16. April 1993. Springer-Verlag, Wien. ga:ANNGA93. [930] John R. Koza. Genetic Programming: On Programming Computers by Means of Natural Selection and Genetics. The MIT Press, Cambridge, MA, 1992. ga:Koza92book. [931] Georges Duponcheele and Derek G. Tilley. Cross-sectional and geometrical shape optimisation by means of genetic algorithm. In Jarmo T. Alander, editor, Proceedings of the First Nordic Workshop on Genetic Algorithms and their Applications (1NWGA), Proceedings of the University of Vaasa, Nro. 2, pages 43–68, Vaasa (Finland), 9.-12. January 1995. University of Vaasa. (available via anonymous ftp site ftp.uwasa.fi directory cs/1NWGA file Duponcheele.ps.Z) ga95aDuponcheele. [932] Francisco J. Varela and Paul Bourgine, editors. Toward a Practice of Autonomous System: Proceedings of the First European Conference on Artificial Life, Paris, 11.-13. December 1991. MIT Press, Cambridge, MA. ga:ECAL91.

82


[933] Celia C. Bojarczuk, Heitor S. Lopes, and Alex A. Freitas. An innovative application of a constrained-syntax genetic programming system to the problem of predicting survival of patients. In Conor Ryan, Terence Soule, Maarten Keijzer, Edward Tsang, Riccardo Poli, and Ernesto Costa, editors, Genetic programming, 6th European Conference, EuroGP 2003 Proceedings, volume 2610 of Lecture Notes in Computer Science, pages 11–21, Essex (UK), 14.-16. April 2003. Springer-Verlag, Berlin. ga03aCCBojarczuk. [934] Peter Nordin and Wolfgang Banzhaf. Genetic reasoning evolving proofs with genetic search. Technical Report SYS-2/96, University of Dortmund, Fachbereich Informatik, 1996. ga96cNordin. [935] Ingo Rechenberg. Evolutionsstrategie ’94. Frommann-Holzboog-Verlag, Stuttgart (Germany), 1994. (in German; includes also [954]) ga94aRechenberg. [936] Stefan Elfwing. Embodied Evolution of Learning Ability. PhD thesis, Kungliga Tekniska h¨ ogskolan,Nada, 2007. ga07bSElfwing. [937] Sankar K. Pal, Ashish Ghosh, and Malay K. Kundu. Soft computing and image analysis: features, relevance and hybridization. pages 1–20. 2000. ga00bSankarKPal. [938] Man Leung Wong and Kwong Sak Leung. Inducing logic programs with genetic algorithms: The genetic logic programming system. IEEE Expert, 10(5):68–76, October 1995. ga95aMLWong. [939] Proceedings of the 1995 IEEE International Conference on Systems, Man and Cybernetics, Vancouver, BC (Canada), 22.-25. October 1995. IEEE, Piscataway, NJ. †EI M042825/96 ga95IEEE-SMC. [940] The Korea Science Engineering Foundation, The Australian Academy of Science, The Australian Academy of Technological Sciences and Engineering. Proceedings of the 1st Korea - Australia Joint Workshop on Evolutionary Computation, Taejon (Korea), 26.-29. September 1995. KAIST, Korea. ga95Korea-Australia. [941] Heinz M¨ uhlenbein and Gerhard Paaß. From recombination of genes to the estimation of distributions: I. binary parameters. In Voigt et al. [921], pages 178–187. ga96aMuhlenbein. [942] Juan R. Velasco and Luis Magdalena. Genetic algorithms in fuzzy control systems. In Winter et al. [920], pages 141–165. ga95aVelasco. [943] Xin Yao, editor. Progress in Evolutionary Computation. Proceedings of the AI’93 and AI’94 Workshops on Evolutionary Computation, volume 956 of Lecture Notes in Artificial Intelligence, Melbourne and Armidale (Australia), 16. November 1993 and 21.-22. November 1994 1995. Springer Verlag, Berlin. †News /Yao ga95Springer956. [944] Terence C. Fogarty?, editor. Evolutionary Computing, Proceedings of the AISB96 Workshop, Brighton, UK, 1.-2. April 1996. Springer-Verlag, Berlin (Germany). †ssq ga96AISB. [945] John R. Koza, editor. Genetic Algorithms and Genetic Programming at Stanford 1997, Stanford, CA, Winter 1997. Stanford University Bookstore. †News /Koza ga97Stanford. [946] Michael Blumenstein, editor. Proceedings of the International Conference on Computational Intelligence and Multimedia Applications, Gold Coast, QUE, Australia, February 1997. Watson Ferguson & Company (Griffith University). †toc /Blumenstein ga97ICCIMA. [947] Christopher G. Langton, Charles Taylor, J. Doyne Farmer, and Steen Rasmussen, editors. Artificial Life II, Proceedings of the Workshop on Artificial Life Held February, 1990 in Santa Fe, New Mexico, Proceedings Volume X, Santa Fe Institute Studies in the Sciences of Complexity. Addison-Wesley, Reading, MA, 1992. ga:ALifeII. [948] Darrell Whitley, editor. Foundations of Genetic Algorithms — 2 (FOGA-92), Vail, CO, 24.-29. July 1992 1993. Morgan Kaufmann: San Mateo, CA. ga:FOGA92. [949] H. Roitblat, Jean-Arcady Meyer, and Stewart W. Wilson, editors. From Animals to Animats, Proceedings of the Second International Conference on Simulation of Adaptive Behavior (SAB92), Honolulu, HI, 7.11. December 1992. The MIT Press, Cambridge, MA. ga:SAB92. [950] John H. Holland. ga:Holland92book.

Adaptation in Natural and Artificial Systems.

MIT Press, Cambridge, 1992.

[951] S. B. Thrun, J. Bala, E. Bloedorn, I. Bratko, B. Cestnik, J Cheng, K. De Jong, S. Dzeroski, D. Fisher, S. E. Fahlman, R. Hamann, K. Kaufman, S. Keller, I. Kononenko, J. Kreuziger, R. S. Michalski, T. Michell, P. Pachowicz, Y. Reich, H. Vafaie, W. Van de Welde, W. Wenzel, J. Wnek, and J. Zhang. The MONK’s problems – a performance comparison of different learning algorithms. Technical Report CMU-CS-91-197, Carnegie Mellon University, School of Computer Science, 1991. ga:JZang91a. [952] Michael O. Odetayo. Replacing one or two individuals at a time during reproduction: An investigation. In Pavel Oˇsmera, editor, Proceedings of the MENDEL’95, pages 99–103, Brno (Czech Republic), 26.-28. September 1995. Technical University of Brno. ga95aOdetayo.

University of Vaasa, Finland

83

[953] Shumeet Baluja. Practical handbook of genetic algorithms. In Chambers [917], chapter 1. Artificial neural network evolution: Learning to steer a land vehicle, pages 31–51. ga95aBaluja. [954] Ingo Rechenberg. Evolutionsstrategie: Optimierung technisher Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog Verlag, Stuttgart, 1973. (reprint in [935]) † ga:Rechenberg73book.

Notations †(ref) = the bibliography item does not belong to my collection of genetic papers. (ref) = citation source code. ACM = ACM Guide to Computing Literature, EEA = Electrical & Electronics Abstracts, BA = Biological Abstracts, CCA = Computers & Control Abstracts, CTI = Current Technology Index, EI = The Engineering Index (A = Annual, M = Monthly), DAI = Dissertation Abstracts International, P = Index to Scientific & Technical Proceedings, PA = Physics Abstracts, PubMed = National Library of Medicine, BackBib = Thomas Bäck’s unpublished bibliography, Fogel/Bib = David Fogel’s EA bibliography, etc * = only abstract seen. ? = data of this field is missing (BiBTeX-format). The last field in each reference item in Teletype font is the BiBTEXkey of the corresponding reference.

84


Appendix A

Bibliography entry formats This documentation was prepared with LATEX and reproduced from camera-ready copy supplied by the editor. The ones who are familiar with BibTeX may have noticed that the references are printed using abbrv bibliography style and have no difficulties in interpreting the entries. For those not so familiar with BibTeX are given the following formats of the most common entry types. The optional fields are enclosed by ”[ ]” in the format description. Unknown fields are shown by ”?”. † after the entry means that neither the article nor the abstract of the article was available for reviewing and so the reference entry and/or its indexing may be more or less incomplete. Book: Author(s), Title, Publisher, Publisher’s address, year. Example

John H. Holland. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, 1975. Journal article: Author(s), Title, Journal, volume(number): first page – last page, [month,] year. Example

David E. Goldberg. Computer-aided gas pipeline operation using genetic algorithms and rule learning. Part I: Genetic algorithms in pipeline optimization. Engineering with Computers, 3(?):35–45, 1987. †. Note: the number of the journal unknown, the article has not been seen. Proceedings article: Author(s), Title, editor(s) of the proceedings, Title of Proceedings, [volume,] pages, location of the conference, date of the conference, publisher of the proceedings, publisher’s address. Example

John R. Koza. Hierarchical genetic algorithms operating on populations of computer programs. In N. S. Sridharan, editor, Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), pages 768–774, Detroit, MI, 20.-25. August 1989. Morgan Kaufmann, Palo Alto, CA. † . Technical report: Author(s), Title, type and number, institute, year. Example

Thomas B¨ ack, Frank Hoffmeister, and Hans-Paul Schwefel. Applications of evolutionary algorithms. Technical Report SYS-2/92, University of Dortmund, Department of Computer Science, 1992.

85

86

Vaasa GA Bibliography

Vaasa GA Bibliography

87

Vaasa Genetic Algorithm Bibliography Search & Optimise Main features:

• Over 19,000 references to published papers • by nearly 20,000 researchers. • Available as over 50 special bibliographies online: ftp://ftp.uwasa.fi/cs/report94-1/ga*bib.ps.Z (compressed PostScript files). New updates also as .pdf files. • Covers all sciences and engineering fields, from basic theory to applications. • Several indexes and statistical summaries. • See what problems evolution can solve for you! Global optimisation and search heuristics called genetic algorithm mimics evolution in nature using recombination and selection from a set of solution trials called population. One of the most prominent attractive features of genetic algorithms from the practical point of view of software techniques is their simplicity, which makes them easy to implement and tailor to solve practical search and optimisation problems. In spite of the seemingly simple processing, the genetic algorithms are good at solving some problems that are known to be hard. The simplicity, generality, flexibility, parallelism, and the good problem solving capability have made genetic algorithm very popular among various disciplines desperately searching methods to solve difficult optimisation problems.

————— Observe that our server has also a selection of our papers on genetic algorithms and other compuational topics. See our bibliographies or file ftp.uwasa.fi/cs/README for further details.

88 file ga90bib.ps.Z . . . ga02bib.ps.Z gaAIbib.ps.Z gaAGRObib.ps.Z gaALIFEbib.ps.Z gaARTbib.ps.Z gaAUSbib.ps.Z gaBASICSbib.ps.Z gaBIObib.ps.Z gaCADbib.ps.Z gaCHEMbib.ps.Z gaCHEMPHYSbib.ps.Z gaCIVILbib.ps.Z gaCODEbib.ps.Z gaCOEVObib.ps.Z gaCONTROLbib.ps.Z gaCSbib.ps.Z gaEARLYbib.ps.Z gaEAST-EURObib.ps.Z gaECObib.ps.Z gaELMAbib.ps.Z gaESbib.ps.Z gaFAR-EASTbib.ps.Z gaFRAbib.ps.Z gaFTPbib.ps.Z gaFUZZYbib.ps.Z gaGEObib.ps.Z gaGERbib.ps.Z gaGPbib.ps.Z gaIMPLEbib.ps.Z gaINDIAbib.ps.Z gaINVERSEbib.ps.Z gaISbib.ps.Z gaJAPANbib.ps.Z gaLCSbib.ps.Z gaLATINbib.ps.Z gaLOGISTICSbib.ps.Z gaMANUbib.ps.Z gaMATHbib.ps.Z gaMEDITERbib.ps.Z gaMILbib.ps.Z gaMLbib.ps.Z gaMSEbib.ps.Z gaNIRbib.ps.Z gaNNbib.ps.Z gaNORDICbib.ps.Z gaOPTICSbib.ps.Z gaOPTIMIbib.ps.Z gaORbib.ps.Z gaPARAbib.ps.Z gaPARETObib.ps.Z gaPATENTbib.ps.Z gaPATTERNbib.ps.Z gaPHYSbib.ps.Z gaPOWERbib.ps.Z gaPROTEINbib.ps.Z gaQCbib.ps.Z gaROBOTbib.ps.Z gaSAbib.ps.Z gaSCHEDULINGbib.ps.Z gaSELECTIONbib.ps.Z gaSIGNALbib.ps.Z gaSIMULAbib.ps.Z gaTELEbib.ps.Z gaTHEORYbib.ps.Z gaTHESESbib.ps.Z gaUKbib.ps.Z gaVLSIbib.ps.Z

Vaasa GA Bibliography # refs . .. 557 2402 102 171 142 634 997 1197 1153 886 2277 848 353 220 1751 1373 720 679 1417 438 455 2066 462 1353 1452 312 1586 918 1291 276 231 81 2343 210 649 630 770 1810 111 897 546 163 1793 849 1571 923 1575 766 406 458 1528 2122 914 472 523 745 307 785 273 2117 939 784 2334 457 1907 792

updated . .. 2007/11/01 2006/02/06 2003/07/09 2003/07/09 2003/07/09 2007/08/23 2005/12/03 2003/07/09 2004/09/20 2003/08/13 2003/05/23 2006/03/27 2007/11/06 2003/07/09 2003/12/10 2003/07/09 2003/09/23 2005/01/21 2003/07/09 2003/07/09 2003/07/09 2003/07/09 2007/11/06 2005/06/30 2004/09/22 2003/07/09 2003/07/09 2003/05/23 2007/08/22 2007/11/01 2003/07/09 2007/11/02 2003/07/09 2003/07/09 2003/07/09 2003/07/09 2005/01/25 2007/11/02 2007/06/28 2007/08/23 2007/11/06 2007/08/23 2007/08/23 2003/07/09 2003/07/09 2003/12/16 2003/07/09 2003/07/09 2007/11/06 2004/09/20 2004/01/19 2004/09/20 2006/03/28 2007/11/01 2003/07/09 2006/09/06 2007/09/20 2004/09/22 2003/07/09 2004/02/26 2005/01/21 2003/07/09 2003/07/09 2006/06/15

contents GA in 1990 . .. GA in 2002 GA in artificial intelligence GA in agriculture (new) GA in artificial life GA in art and music GA in Australia and New Zealand Basics of GA GA in biosciences including medicine GA in Computer Aided Design GA in chemical sciences new; previously in gaCHEMPHYSbib.ps.Z GA in chemistry and physics; divided into gaCHEMbib.ps.Z and gaPHYSbib.ps.Z 2002 GA in civil, structural, and mechanical engineering (new) GA coding) co- and differential evolution GA(new) GA in control and process engineering GA in comp. sci. (incl. databases, /mining, software testing and GP) GA in yearly yeas (upto 1989) new GA in the Eastern Europe new GA in economics and finance GA in electromagnetics Evolution strategies GA in the Far East (excl. Japan) GA in France GA papers available via web (ftp and www) GA and fuzzy logic GA in geosciences GA in Germany, Austria, and Switzerland genetic programming implementations of GA GA in India (new) GA in inverse problems (new: Aug 2007) immune systems GA in Japan Learning Classifier Systems GA in Latin America, Portugal & Spain GA in logistics (incl. TSP) GA in manufacturing GA in mathematics GA in the Mediterranean GA in military applications GA in machine learning new GA in materials new GA in NIRS (spectroscopy) new GA in neural networks GA in Nordic countries GA in optics and image processing GA and optimization (only a few refs) GA in operations research Parallel and distributed GA Pareto optimization (new) GA patents (new) GA in pattern recognition incl. LCS (new) GA in physical sciences new; previously in gaCHEMPHYSbib.ps.Z GA in power engineering GA in protein research quantum computing (new) GA in robotics GA and simulated annealing GA in scheduling Selection in GAs (new) GA in signal and image processing GA in simulation (new) GA in telecom (new) Theory and analysis of GA PhD etc theses GA in United Kingdom GA in electronics, VLSI design and testing

Table A.1: Indexed genetic algorithm special bibliographies available online in directory ftp://ftp.uwasa.fi/cs/report94-1. New updates also as .pdf files.

An Indexed Bibliography of Genetic Algorithms in Machine Learning

An Indexed Bibliography of Genetic Algorithms in Machine Learning

Suggest Documents

An Indexed Bibliography of Genetic Algorithms ...

An Indexed Bibliography of Genetic Algorithms

An Indexed Bibliography of Genetic Algorithms ...

An Indexed Bibliography of Genetic Algorithms in ... - University of Vaasa

An Indexed Bibliography of Genetic Algorithms in ... - University of Vaasa

An Indexed Bibliography of Genetic Algorithms in ... - University of Vaasa

An Indexed Bibliography of Genetic Algorithms in Medicine

An Indexed Bibliography of Genetic Algorithms in ...

An Indexed Bibliography of Genetic Algorithms in Electromagnetics

An Indexed Bibliography of Genetic Algorithms in Telecommunications

An Indexed Bibliography of Genetic Algorithms in Power ... - CiteSeerX

An Indexed Bibliography of Genetic Algorithms in Medicine

An Indexed Bibliography of Genetic Algorithms ... - University of Vaasa

An Indexed Bibliography of Genetic Algorithms ... - University of Vaasa

An Indexed Bibliography of Genetic Algorithm Theses

An Indexed Bibliography of Genetic Algorithm ... - University of Vaasa

An Indexed Bibliography of Co- and Differential

Application of Genetic Algorithms in Machine learning - CiteSeerX

Genetic Algorithms in Search, Optimization, and Machine Learning

Genetic Algorithms in Search Optimization and Machine Learning by ...

Genetic Algorithms in Search Optimization and Machine Learning by ...

Machine Learning Algorithms

Supervised Machine Learning Algorithms

Machine Learning Algorithms - analyticsvidhya.com