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dc.contributor.authorHerrera Triguero, Francisco
dc.contributor.authorLozano, M.
dc.contributor.authorVerdegay, José Luis
dc.date.accessioned2007-03-05T17:24:14Z
dc.date.available2007-03-05T17:24:14Z
dc.date.issued1994
dc.identifier.issn1134-5632
dc.identifier.urihttp://hdl.handle.net/2099/2453
dc.description.abstractGenetic algorithms are adaptive methods that use principles inspired by natural population genetics to evolve solutions to search and optimization problems. Genetic algorithms process a population of search space solutions with three operations: selection, crossover and mutation. A great problem in the use of genetic algorithms is premature convergence; the search becomes trapped in a local optimum before the global optimum is found. Fuzzy logic techniques may be used for solving this problem. This paper presents one of them: the design of crossover operators for real-coded genetic algorithms using fuzzy connectives and its extension based on the use of parameterized fuzzy connectives as tools for tackling the premature convergence problem.
dc.format.extent11
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
dc.relation.ispartofMathware & soft computing . 1994 Vol. 1 Núm. 3 p.239-251
dc.rightsReconeixement-NoComercial-CompartirIgual 3.0 Espanya
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.otherGenetic Algorithms
dc.subject.otherReal Coding
dc.subject.otherFuzzy Connectives
dc.titleThe use of fuzzy connectives to design real-coded genetic algorithms
dc.typeArticle
dc.subject.lemacAlgorismes genètics
dc.subject.amsClassificació AMS::03 Mathematical logic and foundations::03E Set theory
dc.rights.accessOpen Access


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Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain