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Article Dans Une Revue IEEE Transactions on Evolutionary Computation Année : 2024

Bayesian Optimisation for Quality Diversity Search With Coupled Descriptor Functions

Résumé

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 user-defined behavioural metric. In this paper we propose the Bayesian Optimisation of Elites (BOP-Elites) algorithm. Designed for problems with expensive coupled fitness and behaviour functions, 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.
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hal-04537563 , version 1 (08-04-2024)

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Paul Kent, Adam Gaier, Jean-Baptiste Mouret, Juergen Branke. Bayesian Optimisation for Quality Diversity Search With Coupled Descriptor Functions. IEEE Transactions on Evolutionary Computation, 2024, pp.1-1. ⟨10.1109/TEVC.2024.3376733⟩. ⟨hal-04537563⟩
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