Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: A Comparative Study

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dc.contributor.author Martí, Luis
dc.contributor.author García, Jesús
dc.contributor.author Berlanga, Antonio
dc.contributor.author Molina, José M.
dc.date.accessioned 2014-02-19T09:40:41Z
dc.date.available 2014-02-19T09:40:41Z
dc.date.issued 2011
dc.identifier.bibliographicCitation Coello Coello, Carlos A. (ed.), 2011. Learning and Intelligent Optimization: 5th International Conference, LION 5, Rome, Italy, January 17-21, 2011. Selected Papers (Lecture Notes in Computer Science, 6683), Springer, pp. 458-472.
dc.identifier.isbn 978-3-642-25565-6 (print)
dc.identifier.isbn 978-3-642-25566-3 (online)
dc.identifier.issn 0302-9743 (print)
dc.identifier.issn 1611-3349 (online)
dc.identifier.uri http://hdl.handle.net/10016/18289
dc.description Proceedings of: 5th International Conference, LION 5, Rome, Italy, January 17-21, 2011.
dc.description.abstract The 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 a intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work we argue 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 an 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.
dc.description.sponsorship This work was supported by projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartofseries Lecture Notes in Computer Science
dc.relation.ispartofseries 6683
dc.rights © 2011 Springer-Verlag Berlin Heidelberg
dc.title Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: A Comparative Study
dc.type conferenceObject
dc.type bookPart
dc.description.status Publicado
dc.subject.eciencia Informática
dc.identifier.doi 10.1007/978-3-642-25566-3_36
dc.rights.accessRights openAccess
dc.relation.projectID Comunidad de Madrid. S2009/TIC-1485/CONTEXTS
dc.type.version acceptedVersion
dc.relation.eventdate January 17-21, 2011
dc.relation.eventnumber 5
dc.relation.eventplace Rome
dc.relation.eventtitle 5th International Conference Learning and Intelligent Optimization (LION 5)
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 458
dc.identifier.publicationlastpage 472
dc.identifier.publicationtitle Learning and Intelligent Optimization: 5th International Conference, LION 5, Rome, Italy, January 17-21, 2011. Selected Papers
dc.identifier.uxxi CC/0000017288
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