BCMA-ES: A Bayesian approach to CMA-ES - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

BCMA-ES: A Bayesian approach to CMA-ES

Résumé

This 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.
Fichier principal
Vignette du fichier
main.pdf (860.59 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02886512 , version 1 (01-07-2020)

Identifiants

  • HAL Id : hal-02886512 , version 1

Citer

Eric Benhamou, David Saltiel, Sébastien Verel, Fabien Teytaud. BCMA-ES: A Bayesian approach to CMA-ES. GECCO 2020 - The Genetic and Evolutionary Computation Conference, Jul 2020, ONLINE, Mexico. ⟨hal-02886512⟩
53 Consultations
101 Téléchargements

Partager

Gmail Facebook X LinkedIn More