{"title":"A novel genetic cooperative-competitive fuzzy rule based learning method using genetic programming for high dimensional problems","authors":"F. Berlanga, M. J. Jesús, F. Herrera","doi":"10.1109/GEFS.2008.4484575","DOIUrl":null,"url":null,"abstract":"In this contribution, we present GP-COACH, a novel GFS based on the cooperative-competitive learning approach, that uses genetic programming to code fuzzy rules with a different number of variables, for getting compact and accurate rule bases for high dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means of a context-free grammar) and uses a token competition mechanism to maintain the diversity of the population. It makes the rules compete and cooperate among themselves, giving out a compact set of fuzzy rules that presents a good performance. The good results obtained in an experimental study involving several high dimensional classification problems support our proposal.","PeriodicalId":343300,"journal":{"name":"2008 3rd International Workshop on Genetic and Evolving Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 3rd International Workshop on Genetic and Evolving Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GEFS.2008.4484575","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
In this contribution, we present GP-COACH, a novel GFS based on the cooperative-competitive learning approach, that uses genetic programming to code fuzzy rules with a different number of variables, for getting compact and accurate rule bases for high dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means of a context-free grammar) and uses a token competition mechanism to maintain the diversity of the population. It makes the rules compete and cooperate among themselves, giving out a compact set of fuzzy rules that presents a good performance. The good results obtained in an experimental study involving several high dimensional classification problems support our proposal.
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