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Indicator-Based Evolutionary Level Set Approximation: Mixed Mutation Strategy and Extended Analysis

2017-05-27Capítulo de livro Acesso aberto
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datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg07:Energias Renováveis e Acessíveis
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
dc.contributor.authorLiu, Lai-Yee
dc.contributor.authorBasto-Fernandes, Vitor
dc.contributor.authorYevseyeva, Iryna
dc.contributor.authorKok, Joost
dc.contributor.authorEmmerich, Michael
dc.date.accessioned2026-03-24T11:59:52Z
dc.date.available2026-03-24T11:59:52Z
dc.date.issued2017-05-27
dc.description.abstractThe aim of evolutionary level set approximation is to find a finite representation of a level set of a given black box function. The problem of level set approximation plays a vital role in solving problems, for instance in fault detection in water distribution systems, engineering design, parameter identification in gene regulatory networks, and in drug discovery. The goal is to create algorithms that quickly converge to feasible solutions and then achieve a good coverage of the level set. The population based search scheme of evolutionary algorithms makes this type of algorithms well suited to target such problems. In this paper, the focus is on continuous black box functions and we propose a challenging benchmark for this problem domain and propose dual mutation strategies, that balance between global exploration and local refinement. Moreover, the article investigates the role of different indicators for measuring the coverage of the level set approximation. The results are promising and show that even for difficult problems in moderate dimension the proposed evolutionary level set approximation algorithm (ELSA) can serve as a versatile and robust meta-heuristic.eng
dc.identifier.citationLiu, L. -Y., Basto-Fernandes, V., Yevseyeva, I., Kok, J. N., & Emmerich, M. T. M. (2017). Indicator-Based Evolutionary Level Set Approximation: Mixed Mutation Strategy and Extended Analysis. Natural And Artificial Computation For Biomedicine And Neuroscience, 146-159. doi:10.1007/978-3-319-59740-9_15
dc.identifier.doi10.1007/978-3-319-59740-9_15
dc.identifier.isbn9783319597393
dc.identifier.isbn9783319597409
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttp://hdl.handle.net/10400.8/15959
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer International Publishing
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-319-59740-9_15
dc.relation.ispartofLecture Notes in Computer Science
dc.relation.ispartofNatural and Artificial Computation for Biomedicine and Neuroscience
dc.rights.uriN/A
dc.titleIndicator-Based Evolutionary Level Set Approximation: Mixed Mutation Strategy and Extended Analysiseng
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage159
oaire.citation.startPage146
oaire.citation.titleNatural and Artificial Computation for Biomedicine and Neuroscience
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameBasto-Fernandes
person.givenNameVitor
person.identifier.ciencia-id581C-52BB-AC4E
person.identifier.orcid0000-0003-4269-5114
person.identifier.ridN-1891-2016
person.identifier.scopus-author-id53363129900
relation.isAuthorOfPublicationfb2d3703-9d6a-4c22-bbc4-9ff14c162feb
relation.isAuthorOfPublication.latestForDiscoveryfb2d3703-9d6a-4c22-bbc4-9ff14c162feb

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