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dc.contributor.authorRoman, I.
dc.contributor.authorMendiburu, A.
dc.contributor.authorSantana, R.
dc.contributor.authorLozano, J.A. 
dc.date.accessioned2020-07-24T10:13:10Z
dc.date.available2020-07-24T10:13:10Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1135
dc.description.abstractThe optimization of massively multi-modal functions is a challenging task, particularly for problems where the search space can lead the op- timization process to local optima. While evolutionary algorithms have been extensively investigated for these optimization problems, Bayesian Optimization algorithms have not been explored to the same extent. In this paper, we study the behavior of Bayesian Optimization as part of a hybrid approach for solving several massively multi-modal functions. We use well-known benchmarks and metrics to evaluate how different variants of Bayesian Optimization deal with multi-modality.en_US
dc.description.sponsorshipTIN2016-78365-Ren_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectBayesianen_US
dc.subjectOptimizationen_US
dc.subjectmulti-modal problemsen_US
dc.titleBayesian Optimization Approaches for Massively Multi-modal Problemsen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO//SEV-2017-0718en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/Gobierno Vasco/BERC/BERC.2018-2021en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/Gobierno Vasco/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersionen_US
dc.journal.titleLearning and Intelligent Optimizationen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España