Introduction: Genetic fuzzy systems

IEEE Transactions on Fuzzy Systems, 2000

1993

1997

Abstract The automatic de nition of a fuzzy system can be considered in a lot of cases as an optimization or search process. Genetic Algorithms (GAs) are the best known and widely used global search technique with an ability to explore and exploit a given operating space using available performance measures. GAs are known to be capable of nding near optimal solutions in complex search spaces.

2008

Abstract The use of genetic algorithms for designing fuzzy systems provides them with the learning and adaptation capabilities and is called genetic fuzzy systems (GFSs). This topic has attracted considerable attention in the Computation Intelligence community in the last few years.

2004

Fuzzy systems have demonstrated their ability to solve different kinds of problems in various application domains. Currently, there is an increasing interest to augment fuzzy systems with learning and adaptation capabilities. Two of the most successful approaches to hybridise fuzzy systems with learning and adaptation methods have been made in the realm of soft computing.

2007

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.

2009

Abstract Fuzzy inference systems based on fuzzy rule bases (FRBs) have been successfully used to model real problems. Some of the limitations exhibited by these traditional fuzzy inference systems are that there is an abundance of fuzzy operations and operators that an expert should identify. In this paper we present an alternate learning and reasoning schema, which use fuzzy functions instead of if… then rule base structures.

2001

Rendón, The fuzzy classifier system: motivations and first results, Proc. First Intl. Conf. on Parallel Problem Solving from Nature-PPSN I, Springer, Berlin, 1991, pp. 330-334 (scatter Mamdani fuzzy rules for control/modeling problems) M. Valenzuela-Rendón, Reinforcement learning in the fuzzy classifier system, Expert Systems with Applications 14 (1998) 237-247 (scatter Mamdani fuzzy rules for control/modeling problems) J.R. Velasco, Genetic-based on-line learning for fuzzy process control, IJIS 13 (10-11) (1998) 891-903 (scatter Mamdani fuzzy rules for control problems)

1993

F. Herrera, M. Lozano, JL Verdegay Dept. of Computer Science and Arti cial Inteligence University of Granada 18071-Granada, Spain e-mail: herrera, lozano, verdegay@ robinson. ugr. es Keywords: Soft Computing, Genetic Algorithms, Fuzzy Logic, Fuzzy Logic Based Systems.

In this paper the integration of Fuzzy Logic and Genetic Algorithms is discussed. Some potencial Genetic Algorithms applications to fuzzy logic based systems are presented: the generation of the structure of fuzzy IF-THEN rules, the tuning of a fuzzy rules base, and the fuzzy classi er systems.