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...

Full description

Saved in:
Bibliographic Details
Published in:Annals of mathematics and artificial intelligence Vol. 88; no. 1-3; pp. 187 - 212
Main Authors: Gaudrie, David, Le Riche, Rodolphe, Picheny, Victor, Enaux, Benoît, Herbert, Vincent
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.03.2020
Springer
Springer Nature B.V
Springer Verlag
Subjects:
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1012-2443
1573-7470
DOI:10.1007/s10472-019-09644-8