Ten lectures on genetic fuzzy systems

Evolutionary Intelligence, 2009

IEEE Transactions on Fuzzy Systems, 2000

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.

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.

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.

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 ...

Information sciences, 2001

This special issue encompasses 11 papers devoted to the recent developments in the ®eld of``genetic fuzzy systems''. The issue originated from presentations by nine of the authors at the Eighth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU'2000) that was held in Madrid, Spain, July 3±7, 2000, seven of them given in a series of two invited sessions organized by the guest editors of this special issue. These nine original contributions have been thoroughly revised and expanded to become the papers currently presented in this issue. The two remaining contributions have been written by recognized people in the ®eld who were invited by the guest editors.

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.

1993