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Bayesian optimisation for quality diversity search with coupled descriptor functions
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Kent, Paul, Gaier, Adam, Mouret, Jean-Baptiste and Branke, Juergen (2024) Bayesian optimisation for quality diversity search with coupled descriptor functions. IEEE Transactions on Evolutionary Computation . ISSN 1089-778X. (In Press)
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WRAP-Bayesian-optimisation-quality-diversity-search-coupled-descriptor-functions-2024.pdf - Accepted Version - Requires a PDF viewer. Download (2575Kb) | Preview |
Abstract
Quality Diversity (QD) algorithms such as the Multi- Dimensional Archive of Phenotypic Elites (MAP-Elites) are a class of optimisation techniques that attempt to find many high performing points that all behave differently according to a userdefined behavioural metric. In this paper we propose the Bayesian Optimisation of Elites (BOP-Elites) algorithm. Designed for problems with expensive fitness functions and coupled behaviour descriptors, it is able to return a QD solution-set with excellent performance already after a relatively small number of samples. BOP-Elites models both fitness and behavioural descriptors with Gaussian Process surrogate models and uses Bayesian Optimisation strategies for choosing points to evaluate in order to solve the quality-diversity problem. In addition, BOP-Elites produces high quality surrogate models which can be used after convergence to predict solutions with any behaviour in a continuous range. An empirical comparison shows that BOP-Elites significantly outperforms other state-of-the-art algorithms without the need for problem-specific parameter tuning.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > QA Mathematics | ||||||
Divisions: | Faculty of Social Sciences > Warwick Business School > Information Systems & Management Faculty of Social Sciences > Warwick Business School |
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Library of Congress Subject Headings (LCSH): | Bayesian statistical decision theory, Mathematical optimization | ||||||
Journal or Publication Title: | IEEE Transactions on Evolutionary Computation | ||||||
Publisher: | IEEE | ||||||
ISSN: | 1089-778X | ||||||
Official Date: | 2024 | ||||||
Dates: |
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Status: | Peer Reviewed | ||||||
Publication Status: | In Press | ||||||
Re-use Statement: | © 2024 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 1 March 2024 | ||||||
Date of first compliant Open Access: | 4 March 2024 | ||||||
RIOXX Funder/Project Grant: |
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