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dc.contributorUniversitat Ramon Llull. La Salle
dc.contributor.authorBernadó Mansilla, Ester
dc.contributor.authorGarrell i Guiu, Josep Maria
dc.date.accessioned2021-07-01T14:14:51Z
dc.date.accessioned2023-07-13T09:51:41Z
dc.date.available2021-07-01T14:14:51Z
dc.date.available2023-07-13T09:51:41Z
dc.date.issued2000-10
dc.identifier.urihttp://hdl.handle.net/20.500.14342/2857
dc.description.abstractMOLeCS is a new Classifier System which addresses its learning as a multiobjective optimization of two goals: classifier accuracy and generality. These objectives are both emphasized in the fitness evaluation stage, driving the GA search towards the formation of accurate and general rules. We consider severa! existing multiobjec­tive optimization strategies which establish a compro­mise between generality and accuracy in different ways. The system introduces two new proposals which balance both objectives with a bias towards accuracy, resulting in better classification performance. The system also considers a third major objective: covering. It is achieved using sorne niching mechanisms that favour the maintenance of a set of cooperative rules.eng
dc.format.extent10 p.cat
dc.language.isoengcat
dc.publisher3r Congrés Català d'Intel·ligència Artificial, Vilanova i la Geltrú, del 5-7 d'octubre 2000cat
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights© ACIA
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceRECERCAT (Dipòsit de la Recerca de Catalunya)
dc.subject.otherAlgorismes genèticscat
dc.subject.otherAprenentatge automàticcat
dc.titleMultiObjective Learning in a Genetic Classifier System (MOLeCS)cat
dc.typeinfo:eu-repo/semantics/conferenceObjectcat
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.embargo.termscapcat
dc.subject.udc004
dc.subject.udc62


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-nd/4.0/
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