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Efficient dynamic resampling for dominance-based multiobjective evolutionary optimization

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Computación Evolutiva y Redes Neuronales (EVANNAI)es
dc.contributor.authorCervantes, Alejandro
dc.contributor.authorQuintana, David
dc.contributor.authorRecio, Gustavo
dc.date.accessioned2016-06-24T09:48:35Z
dc.date.available2017-06-22T22:00:08Z
dc.date.issued2016-06-22
dc.description.abstractMulti-objective optimization problems are often subject to the presence of objectives that require expensive resampling for their computation. This is the case for many robustness metrics, which are frequently used as an additional objective that accounts for the reliability of specific sections of the solution space. Typical robustness measurements use resampling, but the number of samples that constitute a precise dispersion measure has a potentially large impact on the computational cost of an algorithm. This article proposes the integration of dominance based statistical testing methods as part of the selection mechanism of evolutionary multi-objective genetic algorithms with the aim of reducing the number of fitness evaluations. The performance of the approach is tested on five classical benchmark functions integrating it into two well-known algorithms, NSGA-II and SPEA2. The experimental results show a significant reduction in the number of fitness evaluations while, at the same time, maintaining the quality of the solutions.en
dc.description.sponsorshipThe authors acknowledge financial support granted by the Spanish Ministry of Economy and Competitivity under grant ENE2014-56126-C2-2-R.en
dc.format.extent17
dc.format.mimetypeapplication/pdf
dc.identifier.doi10.1080/0305215X.2016.1187729
dc.identifier.issn0305-215X (print)
dc.identifier.issn1029-0273 (online)
dc.identifier.publicationfirstpage1
dc.identifier.publicationlastpage17
dc.identifier.publicationtitleEngineering optimizationen
dc.identifier.urihttps://hdl.handle.net/10016/23234
dc.language.isoengen
dc.publisherTaylor & Francisen
dc.relation.projectIDGobierno de España. ENE2014-56126-C2-2-Res
dc.relation.publisherversionhttp://dx.doi.org/10.1080/0305215X.2016.1187729es
dc.rights© Taylor & Francis Group, LLCen
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.otherEvolutionary multi-objective optimizationen
dc.subject.otherUncertaintyen
dc.subject.otherResamplingen
dc.titleEfficient dynamic resampling for dominance-based multiobjective evolutionary optimizationen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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