Special issue on genetic fuzzy systems: new advances

1998

This special issue encompasses eight papers devoted to genetic fuzzy systems. All of them are revised and expanded versions of papers presented in a series of two invited sessions organized by the Guest Editors of this special issue at the Seventh International Fuzzy Systems Association World Congress Ž. IFSA'97 that was held in Prague, Czech Republic, June 2529, 1997.

1997

Abstract—Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotics, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. A short description is given in this lecture, introducing their use for machine learning. Key words—genetic algorithms, evolutionary computation, learning.

Advances in fuzzy systems, 2001

like to express their gratitude towards him for the hospitality, support and inspiration they encountered during their stays with the Berkeley Initiative in Soft Computing (BISC). We will keep the wonderful time at Berkeley in keen memory. Most of the ideas (and some of the applications) contained in this book are the results of several (public and private funded) research projects.

1993

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)

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.

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.

1995

This paper proposes two different approaches to apply Genetic Algorithms to Fuzzy Logic Controllers whose Rule Base is defined through a set of rules. The first one uses the knowledge base of the system as the population of the genetic system (a single rule containing the description of the corresponding fuzzy sets is an individual of the population), while the second uses the knowledge base (containing a set of fuzzy rules and a set of Membership functions) as the individual of the genetic system. Both systems have been applied to complex control problems (fossil power plants and biped robots).

Soft Computing-A Fusion of Foundations, Methodologies and Applications, 2011

Since the early 1990s, evolutionary algorithms have been utilized to optimize and learn fuzzy rule-based systems (Cordón et al. 2001a, 2004a; Herrera 2005, 2008; http://sci2s. ugr. es/gfs/). This hybridization is often called evolutionary fuzzy systems (EFSs) or genetic fuzzy systems (GFSs). A list of journal special issues (Herrera 1997; Herrera and Magdalena 1998; Cordón et al. 2001b, 2004b, 2007; Casillas et al. 2007; Carse and Pipe 2007; Casillas and Carse 2009; Alcalá and Nojima 2009) together with a large number of papers related ...