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Quantifying uncertainty with ensembles of surrogates for blackbox optimization

Charles Audet, Sébastien Le Digabel et Renaud Saltet

Rapport technique (2021)

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Abstract

This work is in the context of blackbox optimization where the functions defining the problem are expensive to evaluate and where no derivatives are available. A tried and tested technique is to build surrogates of the objective and the constraints in order to conduct the optimization at a cheaper computational cost. This work proposes different uncertainty measures when using ensembles of surrogates. The resulting combination of an ensemble of surrogates with our measures behaves as a stochastic model and allows the use of efficient Bayesian optimization tools. The method is incorporated in the search step of the mesh adaptive direct search (MADS) algorithm to improve the exploration of the search space. Computational experiments are conducted on seven analytical problems, two multi-disciplinary optimization problems and two simulation problems. The results show that the proposed approach solves expensive simulation-based problems at a greater precision and with a lower computational effort than stochastic models.

Mots clés

blackbox optimization; derivative-free optimization; ensembles of surrogates; mesh adaptive direct search; bayesian optimization

Département: Département de mathématiques et de génie industriel
Centre de recherche: GERAD - Groupe d'études et de recherche en analyse des décisions
Organismes subventionnaires: IVADO Fundamental Research Project Grant
Numéro de subvention: PRF-2019-8079623546
URL de PolyPublie: https://publications.polymtl.ca/48705/
Numéro du rapport: 2021-37
URL officielle: https://www.gerad.ca/fr/papers/G-2021-37
Date du dépôt: 18 avr. 2023 14:59
Dernière modification: 02 nov. 2023 15:31
Citer en APA 7: Audet, C., Le Digabel, S., & Saltet, R. (2021). Quantifying uncertainty with ensembles of surrogates for blackbox optimization. (Rapport technique n° 2021-37). https://www.gerad.ca/fr/papers/G-2021-37

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