Guest Editorial Genetic Fuzzy Systems: What's Next? An ... - IEEE Xplore

IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 15, NO. 4, AUGUST 2007

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Guest Editorial Genetic Fuzzy Systems: What’s Next? An Introduction to the Special Section I. EVOLUTION IN GENETIC FUZZY SYSTEMS’ RESEARCH FTER almost 40 years of development, fuzzy systems [1], [2] have demonstrated their superb ability to solve different problems arising in various application domains. In the last two decades, after Professor Zadeh coined the soft computing term [3], the cooperative framework established made the research interest move to augment fuzzy systems with learning and adaptation capabilities. Since the first pioneer works dating back to 1991 [4]–[7], one of the most successful approaches to hybridize fuzzy systems with learning and adaptation methods, apart of course from fuzzy neural networks, has resulted in so-called genetic fuzzy systems (GFSs) [8], [9]. These computational intelligence techniques augment the approximate reasoning method of fuzzy systems with the learning capabilities of evolutionary algorithms. After more than 15 years of development, GFSs have already left their “emerging” status, because hundreds of papers have been published, special sessions have been organized in conferences, special issues have been edited in journals, and several books have been written and edited. The topic is established enough so as three different stages on the evolution of GFS research can be identified as follows. 1) A first phase, approximately ranging from 1991 to 1997, where the initial settings of the research area were defined. Many contributions were devoted to the fuzzy systems knowledge base learning by adapting the three classical genetic learning approaches (Michigan, Pittsburgh, and Iterative Rule Learning) to derive fuzzy rules for different types of fuzzy-rule based systems, and for different applications (fuzzy control, fuzzy modeling, and fuzzy classification). Besides, the first proposals to tune membership functions also appeared. 2) A second phase, starting around 1995 and still remaining active, can be identified being specifically focused on fuzzy systems tuning by evolutionary algorithms. The importance of these procedures to improve the performance of fuzzy systems was recognized and new data base components were progressively adapted apart from the originally considered fuzzy membership functions (scaling factors, context adaptation, linguistic hedges, ). Recently, a strong interest on incorporating the comprehensibility of the fuzzy systems into the tuning process has appeared.

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Digital Object Identifier 10.1109/TFUZZ.2007.900902

3) We finally found a third stage mainly based on the proposal of new GFS learning approaches, whose start can be established around 1998. It comprises several branches ranging from the less innovative use of new, nonclassical evolutionary algorithms, to the design of specific GFSs to deal with the important interpretability-accuracy trade-off problem in fuzzy modeling. Besides, much effort has been put on the design of hybrid learning approaches (a priori database learning, genetic fuzzy neural networks, Michigan-Pittsburgh hybrids, ); on the use of multi-objective evolutionary algorithms; and on the design of GFSs capable of dealing with the curse of dimensionality for complex problems with high dimensionality and/or large data sets. II. STATE-OF-THE-ART IN GENETIC FUZZY SYSTEMS AND SEED OF THE SPECIAL SECTION In view of the latter, it can be recognized how the GFS research area has reached its maturity and how many specific topics have sound roots and have been largely developed. Due to this fact, by the beginning of 2004, a specific Task Force on “Genetic Fuzzy Systems” was created within the Fuzzy Systems Technical Committee, from the IEEE Computational Intelligence Society, being chaired since then by Oscar Cordón, Co-Guest Editor of the current special section, and formed by representative researchers on the topic: Plamen Angelov, Piero Bonissone, Brian Carse, Fernando Gomide, Francisco Herrera, Frank Hoffmann, Hisao Ishibuchi, Luis Magdalena, and Witold Pedrycz. Among the different activities of the Task Force, a Series of Workshops on the topic has been established, with a Program Committee composed of recognized researchers on the area, to facilitate and focus the discussion on the problems, research, results and future directions in GFSs. The series started in Granada, Spain, with the First International Workshop on Genetic Fuzzy Systems (GFS2005) (http://sci2s.ugr.es/~gfs2005) celebrated between the 17th and the 19th of March, 2005, went on with the 2006 International Symposium on Evolving Fuzzy Systems (EFS’06) (http://www.efs06.org/) held in Lake District, UK, between the 7th and the 9th of September, 2006, and will continue with the Third International Workshop on Genetic and Evolving Fuzzy Systems (GEFS08) (http://www.gefs08.de) to be organized next March, 4–7, 2008 in Witten-Bommerholz, Germany. A round table was scheduled for each Workshop to discuss on the future of GFSs. The first one celebrated in Granada in

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IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 15, NO. 4, AUGUST 2007

2005 was especially interesting, as many of the experts in the field (including seven of the ten Task Force members) attended to it and gave their view. Some provocative and critic questions were posed on what had been done on the topic till that time, its actual competence, the errors made, and which could be its future. Some open research lines such as the following ones were identified: • new evolutionary learning approaches and coding schemes for GFSs; • scaling up of GFSs to high-dimensional problems; • new real-world application areas not previously tackled with GFSs; • other significantly innovative GFS proposals not based on the usual learning and/or tuning schemes. As a conclusion of all of this, we thought, it became timely to edit a special section (the current one) that could serve as a seed for the fourth stage of development of GFSs, and the proposal was made, accepted and the call for papers launched in 2006, coinciding with the fifteenth anniversary of the first publications on the discipline. III. THE PAPERS IN THE SPECIAL ISSUE As said, our aim was to have a very closed scope only considering new contributions that could actually mean a significant advancement on the GFS research area. This is why we chose a provocative title as “Genetic Fuzzy Systems: What’s Next?”. In order to ensure the real novelty of the contributions, the guest editors would check each of them in order to determine if its contents were innovative enough to actually fit within the challenging scope. On the other hand, to check the high quality of the submissions, three independent reviewers (whose work and cooperation we want to acknowledge and thank) and one of the guest editors carefully revised each paper. Thirteen contributions were submitted to the issue, two of which were directly discarded by the guest editors because they were out of the very closed scope of “next-generation GFSs”. After the first tough review round, only three papers were accepted with minor modifications, six of them were rejected, and the remaining two were asked for major revision. Once the second round was finished, only four of the 13 submitted contributions were accepted for publication. The four papers in this special section address distinct subjects focusing on new, significant novel lines of development on GFSs. In the first one, entitled “Incremental Evolutionary Design of TSK Fuzzy Controllers”, F. Hoffmann et al. deal with the first novel research line mentioned in the previous subsection, “New learning approaches and coding schemes for GFSs”, by proposing a novel GFS to design zero-order TSK fuzzy controllers based on an incremental learning scheme. The key idea of the approach is to apply the existing concept of fuzzy models gradual refinement to the incremental design of TSK fuzzy controllers for nonlinear systems. Starting from a single optimal TSK fuzzy rule, corresponding to a single linear control law, the controller structure is gradually refined by splitting this general rule that covers the entire input space into local fuzzy rules with

different gain factors in different regions of the state space. The structural augmentation wraps an evolutionary process adapting the additional parameters associated to the new rules, with the joint objective of improving the control performance and to maximize the stability region of the nonlinear system. The proposal is evaluated in two different control problems, obtaining a good performance and being able to optimize fuzzy state space partitions and gains for hundreds of rules, what can not be done by classical pure parameter optimization of the gains of the TSK system. Another contribution in the same topic is that by J. Casillas et al., entitled “Fuzzy XCS: A Michigan Genetic Fuzzy System”, where a fuzzy XCS system for single-step reinforcement problems is proposed. It constitutes a significant proposal since GFSs based on the Michigan rule learning approach are somehow underdeveloped, while their crisp counterparts, learning classifier systems, constitute an extremely active area in the evolutionary computation community. This could be due to the difficulty in extending the discrete-valued system operation to the continuous case. In the current paper, the authors solve the latter problem by taking inspiration from the classical XCS learning classifier system to design a new accuracy-based reward system, capable of interacting with the environment by means of continuous actions to learn through reinforcement. The consideration of generalized rules in the fuzzy system expressing the state-action relationships allows more compact rule bases to be derived, with an easier scalability to higher dimensional spaces, a faster inference, and a better linguistic interpretability, thus composing a good solution to the interpretability-accuracy tradeoff. The paper “Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing” by M. J. del Jesus et al. is related to the second and third novel research lines, namely “Scaling up of GFSs to high-dimensional problems” and “New real-world application areas”. In this contribution, an evolutionary technique is proposed to learn DNF fuzzy rules for subgroup discovery, one of the existing data mining and knowledge discovery disciplines, what up to our knowledge constitutes the first application of a GFS in this area. Since there is a need that the individual rules describing the subgroups are both relatively simple and clearly interpretable for the user, the use of fuzzy sets to represent the existing associations in the data facilitates the interpretation of the DNF rules through their linguistic terms and avoids unnatural boundaries in the attribute domains partitioning. The proposal is tested against a classical approach, CN2 for subgroup discovery, in standard databases, and is also applied to a real-world marketing problem where the extracted knowledge is used to define trade fairs planning policies obtaining outstanding results. Finally, L. Sánchez and I. Couso focus on the fourth open line, “Other significantly innovative GFS proposals”, in the last contribution to the issue entitled “Advocating the Use of Imprecisely Observed Data in Genetic Fuzzy Systems”. In our opinion, this is one of the most novel developments in the area

IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 15, NO. 4, AUGUST 2007

in the last few years. It takes, as a base, the fact that many different real-world problems deal with inherently fuzzy (uncertain and vague) data and that, by now, fuzzy systems have mainly focused on fuzzy processing of crisp data for control, modeling and classification applications, and substituting or complementing other, more classical techniques, such as statistical approaches. However, as the authors state, GFSs have the capability of directly handling fuzzy data, thus not losing valuable problem information, while the most of the remaining techniques lack of this ability. With this in mind, they propose a framework to automatically derive GFSs from imprecisely measured data and demonstrate its good performance in several realworld applications.

ACKNOWLEDGMENT The Guest Editors would first like to thank the contributors who answered our call and the reviewers for their careful work in making this special section possible. They would also like to express their gratitude to the IEEE CIS Fuzzy Systems Technical Committee Chair, Jerry Mendel, and especially thank Editor-in-Chief Nik Pal of the IEEE TRANSACTIONS ON FUZZY SYSTEMS for providing us the opportunity to edit this section. The Guest Editors appreciate their kind help during the editorial process. OSCAR CORDÓN, Guest Editor European Centre for Soft Computing Mieres 33600, Spain [email protected] and also with Department of Computer Science and Artificial Intelligence, University of Granada Granada 18071, Spain [email protected]

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RAFAEL ALCALÁ, Guest Editor Department of Computer Science and Artificial Intelligence, University of Granada Granada 18071, Spain [email protected] JESÚS ALCALÁ-FDEZ, Guest Editor Department of Computer Science, University of Jaén Jaén 23071, Spain [email protected] IGNACIO ROJAS, Guest Editor Department of Computer Architecture and Computer Technology, University of Granada Granada 18071, Spain [email protected] REFERENCES [1] L. A. Zadeh, “Fuzzy sets,” Inf. Contr., vol. 8, pp. 338–353, 1965. [2] L. A. Zadeh, “Outline of a new approach to the analysis of complex systems and decision processes,” IEEE Trans. Syst., Man, Cybern., vol. 3, pp. 28–44, 1973. [3] L. A. Zadeh, “Fuzzy logic and soft computing: Issues, contentions and perspectives,” in Proc. Third Int. Conf. Fuzzy Logic, Neural Nets and Soft Computing (IIZUKA’94), Iizuka, Japan, 1994, pp. 1–2. [4] C. Karr, “Genetic algorithms for fuzzy controllers,” AI Expert, vol. 6, no. 2, pp. 26–33, 1991. [5] D. T. Pham and D. Karaboga, “Optimum design of fuzzy logic controllers using genetic algorithms,” J. Syst. Eng., vol. 1, pp. 114–118, 1991. [6] P. Thrift, “Fuzzy logic synthesis with genetic algorithms,” in Proc. Fourth Int. Conf. Genetic Algorithms (ICGA’91), San Diego, CA, 1991, pp. 509–513. [7] M. Valenzuela-Rendón, “The fuzzy classifier system: Motivations and first results,” in Proc. First Int. Conf. Parallel Probl. Solving From Nature—PPSN I, H. P. Schwefel and R. Männer, Eds., Berlin, Germany, 1991, pp. 330–334. [8] O. Cordón, F. Herrera, F. Hoffmann, and L. Magdalena, Genetic Fuzzy Systems. Evolutionary Tuning and Learning of Fuzzy Knowledge Bases, ser. Advances in Fuzzy Systems—Applications and Theory Series. Singapore: World Scientific, 2001, vol. 19. [9] O. Cordón, F. Gomide, F. Herrera, F. Hoffmann, and L. Magdalena, “Ten years of genetic fuzzy systems: Current framework and new trends,” Fuzzy Sets Syst., vol. 141, no. 1, pp. 5–31, 2004.