Scheduling Exploration/Exploitation Levels in Genetically-Generated Fuzzy Knowledge

Sofiane Achiche, Luc Baron, Marek Balazinski

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

In this paper we study the influence of the exploration/exploitation balance on the performances of a real binary/like genetic algorithm in automatically generating fuzzy knowledge bases from a set of numerical data. The influence is explored through different scheduling of crossover strategies throughout the evolution process. The aim of this paper is to prove the influence of a good balance between exploration and exploitation levels on the performances of the optimization algorithm used, along with the influence of a good definition of the early stages versus the late stages of the evolution.
Original languageEnglish
Title of host publicationAnnual Meeting of the North American Fuzzy Information Processing Society (IEEE)
Volume1
Publication date2004
Pages401-406
Publication statusPublished - 2004
Externally publishedYes
Event2004 Annual Meeting of the North American Fuzzy Information Processing Society - Banff, Canada
Duration: 27 Jun 200430 Jun 2004

Conference

Conference2004 Annual Meeting of the North American Fuzzy Information Processing Society
CountryCanada
CityBanff
Period27/06/200430/06/2004

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