Targeting solutions in Bayesian multi-objective optimization: sequential and batch versions
Multi-objective optimization aims at finding trade-off solutions to conflicting objectives. These constitute the Pareto optimal set. In the context of expensive-to-evaluate functions, it is impossible and often non-informative to look for the entire set. As an end-user would typically prefer a certa...
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| Published in | Annals of mathematics and artificial intelligence Vol. 88; no. 1-3; pp. 187 - 212 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Springer International Publishing
01.03.2020
Springer Springer Nature B.V Springer Verlag |
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| ISSN | 1012-2443 1573-7470 |
| DOI | 10.1007/s10472-019-09644-8 |
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| Abstract | Multi-objective optimization aims at finding trade-off solutions to conflicting objectives. These constitute the Pareto optimal set. In the context of expensive-to-evaluate functions, it is impossible and often non-informative to look for the entire set. As an end-user would typically prefer a certain part of the objective space, we modify the Bayesian multi-objective optimization algorithm which uses Gaussian Processes and works by maximizing the Expected Hypervolume Improvement, to focus the search in the preferred region. The cumulated effects of the Gaussian Processes and the targeting strategy lead to a particularly efficient convergence to the desired part of the Pareto set. To take advantage of parallel computing, a multi-point extension of the targeting criterion is proposed and analyzed. |
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| AbstractList | Multi-objective optimization aims at finding trade-off solutions to conflicting objectives. These constitute the Pareto optimal set. In the context of expensive-to-evaluate functions, it is impossible and often non-informative to look for the entire set. As an end-user would typically prefer a certain part of the objective space, we modify the Bayesian multi-objective optimization algorithm which uses Gaussian Processes and works by maximizing the Expected Hypervolume Improvement, to focus the search in the preferred region. The cumulated effects of the Gaussian Processes and the targeting strategy lead to a particularly efficient convergence to the desired part of the Pareto set. To take advantage of parallel computing, a multi-point extension of the targeting criterion is proposed and analyzed. Multi-objective optimization aims at finding trade-off solutions to conflicting objectives. These constitute the Pareto optimal set. In the context of expensive-to-evaluate functions, it is impossible and often non-informative to look for the entire set. As an end-user would typically prefer a certain part of the objective space, we modify the Bayesian multi-objective optimization algorithm which uses Gaussian Processes and works by maximizing the Expected Hypervolume Improvement, to focus the search in the preferred region. The cumulated effects of the Gaussian Processes and the targeting strategy lead to a particularly efficient convergence to the desired part of the Pareto set. To take advantage of parallel computing, a multi-point extension of the targeting criterion is proposed and analyzed. Keywords Gaussian processes * Bayesian optimization * Computer experiments * Preference-based optimization * Parallel optimization Mathematics Subject Classification (2010) 65Kxx Multi-objective optimization aims at finding trade-off solutions to conflicting objectives. These constitute the Pareto optimal set. In the context of expensive-to-evaluate functions, it is impossible and often non-informative to look for the entire set. As an end-user would typically prefer a certain part of the objective space, we modify the Bayesian multi-objective optimization algorithm which uses Gaussian Processes to maximize the Expected Hypervolume Improvement, to focus the search in the preferred region. The cumulated effects of the Gaussian Processes and the targeting strategy lead to a particularly efficient convergence to the desired part of the Pareto set. To take advantage of parallel computing, a multi-point extension of the targeting criterion is proposed and analyzed. |
| Audience | Academic |
| Author | Enaux, Benoît Le Riche, Rodolphe Picheny, Victor Herbert, Vincent Gaudrie, David |
| Author_xml | – sequence: 1 givenname: David orcidid: 0000-0003-4609-1457 surname: Gaudrie fullname: Gaudrie, David email: david.gaudrie@mpsa.com organization: Groupe PSA, CNRS LIMOS, École Nationale Supérieure des Mines de Saint-Étienne – sequence: 2 givenname: Rodolphe surname: Le Riche fullname: Le Riche, Rodolphe organization: CNRS LIMOS, École Nationale Supérieure des Mines de Saint-Étienne – sequence: 3 givenname: Victor surname: Picheny fullname: Picheny, Victor organization: Prowler.io – sequence: 4 givenname: Benoît surname: Enaux fullname: Enaux, Benoît organization: Groupe PSA – sequence: 5 givenname: Vincent surname: Herbert fullname: Herbert, Vincent organization: Groupe PSA |
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| Keywords | Preference-based optimization Computer experiments Bayesian optimization Parallel optimization 65Kxx Gaussian processes |
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