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Multi-objective optimization with an adaptive resonance theory-based estimation of distribution algorithm

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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, José M.
dc.date.accessioned2014-07-03T11:03:45Z
dc.date.available2014-08-01T22:00:05Z
dc.date.issued2013-08
dc.description.abstractThe introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have an intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work, we put forward the argument that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and a hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.es
dc.description.sponsorshipThis work was supported by projects Projects CICYT TIN2011-28620-C02- 01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02.es
dc.description.statusPublicadoes
dc.format.extent27es
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationAnnals of Mathematics and Artificial Intelligence (2013), 68 (4), 247-273.es
dc.identifier.doi10.1007/s10472-012-9303-0
dc.identifier.issn1012-2443 (print)
dc.identifier.issn1573-7470 (online)
dc.identifier.publicationfirstpage247es
dc.identifier.publicationissue4es
dc.identifier.publicationlastpage273es
dc.identifier.publicationtitleAnnals of Mathematics and Artificial Intelligencees
dc.identifier.publicationvolume68es
dc.identifier.urihttps://hdl.handle.net/10016/19048
dc.identifier.uxxiAR/0000014564
dc.language.isoenges
dc.publisherSpringeres
dc.relation.projectIDComunidad de Madrid. S2009/TIC-1485/CONTEXTSes
dc.relation.projectIDTEC2011-28626-C02-02
dc.relation.publisherversionhttp://dx.doi.org/10.1007/s10472-012-9303-0es
dc.rights© 2012 Springer Science+Business Media B.Ves
dc.rights.accessRightsopen accesses
dc.subject.ecienciaInformáticaes
dc.subject.otherMulti-objective optimizationen
dc.subject.otherEstimation of Distribution Algorithmsen
dc.subject.otherAdaptive Resonance theoryen
dc.subject.otherMulti-objective Optimizationen
dc.subject.otherEvolutionary Algorithmen
dc.subject.otherNeural Networken
dc.subject.otherHypervolumeen
dc.subject.otherModelen
dc.titleMulti-objective optimization with an adaptive resonance theory-based estimation of distribution algorithmen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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