Genetic fuzzy systems. New developments

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.

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.

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.

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.

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 ...

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

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.

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.

Abstract Contributors to the special track on Evolutionary Fuzzy Systems at the EUSFLAT 2003 conference were asked to record their thoughts and ideas on the current state of evolutionary fuzzy systems research," burning issues" and future directions. This paper brings together these contributions. Keywords: Evolutionary Algorithms. Fuzzy Systems.