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Moving away from error-based learning in multi-objective estimation of distribution algorithms

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA)es
dc.contributor.authorMartí, Luis
dc.contributor.authorGarcía, Jesús
dc.contributor.authorBerlanga de Jesús, Antonio
dc.contributor.authorMolina López, José Manuel
dc.date.accessioned2014-03-28T11:32:44Z
dc.date.available2014-03-28T11:32:44Z
dc.date.issued2010-07
dc.descriptionProceedings of: 12th annual conference on Genetic and evolutionary computation (GECCO '10). Portland, Oregon, USA, July 7-11, 2010.es
dc.description.abstractIn this work we analyze the model-building issue and the requirements it imposes on the learning paradigm being used. We argue that error-based learning, the class of learning most commonly used in MOEDAs, is responsible for current MOEDA underachievement. We present ART as a viable alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and an hypervolume based selector as described for the HypE algorithm. We experimentally show that thanks to MARTEDA's novel model-building approach and an indicator-based population ranking the algorithm it is able to outperform similar MOEDAs and MOEAs.es
dc.description.sponsorshipThis work was supported in part by projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, DPS2008-07029-C02-02 and CAM CONTEXTS S2009/TIC-1485.es
dc.description.statusPublicadoes
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationGECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation. (2010) ACM, New York, USA, 545-546es
dc.identifier.doi10.1145/1830483.1830585
dc.identifier.isbn978-1-4503-0072-8
dc.identifier.publicationfirstpage545
dc.identifier.publicationlastpage546
dc.identifier.publicationtitleGECCO 2010 : Genetic and Evolutionary Computation Conference : Wednesday-Sunday, July 7-11, 2010, Portland, Oregon
dc.identifier.urihttps://hdl.handle.net/10016/18667
dc.identifier.uxxiCC/0000011615
dc.language.isoenges
dc.publisherACMes
dc.relation.eventdateJuly 7-11, 2010es
dc.relation.eventnumber12
dc.relation.eventplacePortland, Oregon, USAes
dc.relation.eventtitleConference on Genetic and evolutionary computation (GECCO '10)es
dc.relation.projectIDComunidad de Madrid. S2009/TIC-1485/CONTEXTSes
dc.rights© ACM, 2010es
dc.rights.accessRightsopen accesses
dc.subject.ecienciaInformáticaes
dc.subject.otherMulti-objective Optimizationes
dc.subject.otherEstimation of Distribution Algorithmses
dc.subject.otherAdaptive Resonance Theoryes
dc.titleMoving away from error-based learning in multi-objective estimation of distribution algorithmses
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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