Global optimization based on noisy evaluations: an empirical study of two statistical approaches - CentraleSupélec Accéder directement au contenu
Article Dans Une Revue Journal of Global Optimization Année : 2008

Global optimization based on noisy evaluations: an empirical study of two statistical approaches

Résumé

The optimization of the output of complex computer codes has often to be achieved with a small budget of evaluations. Algorithms dedicated to such problems have been developed and compared, such as the Expected Improvement algorithm (EI) or the Informational Approach to Global Optimization (IAGO). However, the influence of noisy evaluation results on the outcome of these comparisons has often been neglected, despite its frequent appearance in industrial problems. In this paper, empirical convergence rates for EI and IAGO are compared when an additive noise corrupts the result of an evaluation. IAGO appears more efficient than EI and various modifications of EI designed to deal with noisy evaluations.
Fichier principal
Vignette du fichier
paper_submitted_v2.pdf (434.08 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-00354656 , version 1 (20-01-2009)
hal-00354656 , version 2 (17-03-2009)

Identifiants

Citer

Emmanuel Vazquez, Julien Villemonteix, Maryan Sidorkiewicz, Eric Walter. Global optimization based on noisy evaluations: an empirical study of two statistical approaches. Journal of Global Optimization, 2008, Vol. 43 ((2-3)), pp. 373-389. ⟨10.1007/s10898-008-9313-y⟩. ⟨hal-00354656v1⟩

Collections

SUP_SYSTEMES
201 Consultations
1868 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More