AN OVERVIEW OF TECHNIQUES FOR GENETIC EVOLUTION OF FUZZY SYSTEMS

J. Lambert, M. Schneider and A. Kandel
Additional contact information
J. Lambert: University of South Florida
A. Kandel: Netanya Academic College

Fuzzy Economic Review, 2003, vol. VIII, issue 1, 3-12

Abstract: Genetic algorithms have recently gained notoriety as search engines of remarkable power, successfully search-ing, in reasonably short times, spaces completely intractable to most traditio-nal methods. The application of the genetic algorithm to the difficult problem of fuzzy system optimization has revealed that they are capable of refining nearly every aspect of the fuzzy system, generating near-optimal con-trollers, which exceed the capabilities of both hand designed and neurally optimized systems. A variety of research has begun to indicate that the genetic algorithm has the potential of generating highly optimized controllers without receiving any expert input whatsoever. In this paper we examine techniques used in applying the genetic algorithm to the optimization of a variety of fuzzy systems.

Keywords: Fuzzy Techniques; Genetic Evolution; Integrated Fuzzy Systems; Imprecise Decision Making (search for similar items in EconPapers)
JEL-codes: C45 (search for similar items in EconPapers)
Date: 2003
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fzy:fuzeco:v:viii:y:2003:i:1:p:3-12

Access Statistics for this article

More articles in Fuzzy Economic Review from International Association for Fuzzy-set Management and Economy (SIGEF) Contact information at EDIRC.
Bibliographic data for series maintained by Aurelio Fernandez ( this e-mail address is bad, please contact ).