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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Vydáno v:Annals of mathematics and artificial intelligence Ročník 88; číslo 1-3; s. 187 - 212
Hlavní autoři: Gaudrie, David, Le Riche, Rodolphe, Picheny, Victor, Enaux, Benoît, Herbert, Vincent
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.03.2020
Springer
Springer Nature B.V
Springer Verlag
Témata:
Algorithms
Approximation
Artificial Intelligence
Bayesian analysis
Complex Systems
Computer Science
Design of experiments
Expected values
Gaussian process
Mathematics
Multiple objective analysis
Objectives
Optimization
Optimization and Control
Other Statistics
Pareto optimization
Pareto optimum
Statistics
Preference-based optimization
Computer experiments
Bayesian optimization
Parallel optimization
65Kxx
Gaussian processes
ISSN:1012-2443, 1573-7470
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:1012-2443
1573-7470
DOI:10.1007/s10472-019-09644-8