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hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorBenhamou, Éric
dc.contributor.authorSaltiel, David
dc.contributor.authorVerel, Sébastien
HAL ID: 2072
ORCID: 0000-0003-1661-4093
dc.contributor.authorTeytaud, Fabien
HAL ID: 10819
dc.date.accessioned2020-11-12T10:28:17Z
dc.date.available2020-11-12T10:28:17Z
dc.date.issued2020
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/21201
dc.language.isoenen
dc.subjectCMA-ESen
dc.subjectBayesianen
dc.subjectconjugate prioren
dc.subjectnormal-inverse-Wisharten
dc.subject.ddc519en
dc.titleBCMA-ES: A Bayesian approach to CMA-ESen
dc.typeDocument de travail / Working paper
dc.description.abstractenThis paper introduces a novel theoretically sound approach for the celebrated CMA-ES algorithm. Assuming the parameters of the multi variate normal distribution for the minimum follow a conjugate prior distribution, we derive their optimal update at each iteration step. Not only provides this Bayesian framework a justification for the update of the CMA-ES algorithm but it also gives two new versions of CMA-ES either assuming normal-Wishart or normal-Inverse Wishart priors, depending whether we parametrize the likelihood by its covariance or precision matrix. We support our theoretical findings by numerical experiments that show fast convergence of these modified versions of CMA-ES.en
dc.relation.ispartofseriestitlePreprint Lamsadeen
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-02886512en
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.date.updated2020-11-12T10:22:50Z
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