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dc.contributor.authorGonzález Muñoz, Antonio
dc.contributor.authorHerrera Triguero, Francisco
dc.date.accessioned2007-09-17T11:53:22Z
dc.date.available2007-09-17T11:53:22Z
dc.date.issued1997
dc.identifier.issn1134-5632
dc.identifier.urihttp://hdl.handle.net/2099/3495
dc.description.abstractGenetic algorithms (GAs) represent a class of adaptive search techniques inspired by natural evolution mechanisms. The search properties of GAs make them suitable to be used in machine learning processes and for developing fuzzy systems, the so-called genetic fuzzy systems (GFSs). In this contribution, we discuss genetics-based machine learning processes presenting the iterative rule learning approach, and a special kind of GFS, a multi-stage GFS based on the iterative rule learning approach, by learning from examples.
dc.format.extent233-249
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
dc.relation.ispartofMathware & soft computing . 1997 Vol. 4 Núm. 3
dc.rightsReconeixement-NoComercial-CompartirIgual 3.0 Espanya
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.otherFuzzy logic
dc.subject.otherFuzzy rules
dc.subject.otherGenetic algoritms
dc.subject.otherMachine learning
dc.subject.otherGFS
dc.subject.otherGenetic fuzzy systems
dc.titleMulti-stage genetic fuzzy systems based on the iterative rule learning approach
dc.typeArticle
dc.subject.lemacIntel·ligència artificial
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacSistemes difusos
dc.subject.amsClassificació AMS::68 Computer science::68T Artificial intelligence
dc.rights.accessOpen Access


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