Efficient dynamic resampling for dominance-based multiobjective evolutionary optimization

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dc.contributor.author Cervantes, Alejandro
dc.contributor.author Quintana, David
dc.contributor.author Recio, Gustavo
dc.date.accessioned 2020-08-06T08:34:22Z
dc.date.available 2020-08-06T08:34:22Z
dc.date.issued 2017-02-01
dc.identifier.bibliographicCitation Alejandro Cervantes, David Quintana & Gustavo Recio. (2017). Efficient dynamic resampling for dominance-based multiobjective evolutionary optimization, Engineering Optimization, 49:2, 311-327
dc.identifier.issn 0305-215X
dc.identifier.uri http://hdl.handle.net/10016/30752
dc.description.abstract Multi-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.
dc.description.sponsorship The authors acknowledge financial support granted by the Spanish Ministry of Economy and Competitivity [grant ENE2014-56126-C2-2-R].
dc.language.iso eng
dc.publisher Taylor & Francis Online
dc.rights © 2020 Informa UK Limited
dc.subject.other Uncertainty
dc.subject.other Resampling
dc.subject.other Algorithms
dc.subject.other Robustness
dc.subject.other Evolutionary multi-objective optimization
dc.subject.other Portfolio optimization
dc.title Efficient dynamic resampling for dominance-based multiobjective evolutionary optimization
dc.type article
dc.subject.eciencia Informática
dc.identifier.doi https://doi.org/10.1080/0305215X.2016.1187729
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. ENE2014-56126-C2-2-R
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 311
dc.identifier.publicationissue 2
dc.identifier.publicationlastpage 327
dc.identifier.publicationtitle ENGINEERING OPTIMIZATION
dc.identifier.publicationvolume 49
dc.identifier.uxxi AR/0000018572
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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