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dc.contributor.authorDrobics, Mario
dc.contributor.authorBotzheim, J.
dc.date.accessioned2013-04-12T19:10:10Z
dc.date.available2013-04-12T19:10:10Z
dc.date.issued2008
dc.identifier.citationDrobics, Mario; Botzheim, J. Optimization of fuzzy rule sets using a bacterial evolutionary algorithm. "Mathware & Soft Computing", vol. 15, núm. 1, p. 21-40.
dc.identifier.issn1134-5632
dc.identifier.urihttp://hdl.handle.net/2099/13175
dc.description.abstractIn this paper we present a novel approach where we rst create a large set of (possibly) redundant rules using inductive rule learning and where we use a bacterial evolutionary algorithm to identify the best subset of rules in a subsequent step. This enables us to nd an optimal rule set with respect to a freely de nable global goal function, which gives us the possibility to integrate interpretability related quality criteria explicitly in the goal function and to consider the interplay of the overlapping fuzzy rules
dc.format.extent20 p.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
dc.relation.ispartofMathware & Soft Computing. 2008, vol. 15, núm. 1
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Informàtica teórica
dc.subject.lcshArtificial intelligence
dc.titleOptimization of fuzzy rule sets using a bacterial evolutionary algorithm
dc.typeArticle
dc.subject.lemacIntel•ligència artificial
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::68 Computer science::68T Artificial intelligence
dc.rights.accessOpen Access
local.citation.authorDrobics, Mario; Botzheim, J.
local.citation.publicationNameMathware & Soft Computing
local.citation.volume15
local.citation.number1
local.citation.startingPage21
local.citation.endingPage40


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