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Publication 2023

BOP-Elites

A Bayesian Optimisation Approach to Quality Diversity Search with Black-Box descriptor functions

Fig. 3. A visualisation of the BOP-Elites algorithm

Abstract

BOP-Elites: A Bayesian Optimisation Approach to Quality Diversity Search with Black-Box descriptor functions

Paul Kent, Adam Gaier, Juergen Branke, Jean-Baptiste Mouret

Quality Diversity (QD) algorithms such as 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 black-box objective and behaviour functions, it is able to return a QD solution-set after a relatively small number of samples. BOP-Elites models both objective 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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Associated Researchers

Paul Kent

Warwick University

Adam Gaier

Sr. Principal AI Research Scientist & Sr. Manager

Jean-Baptiste Mouret

Inria, CNRS, Université de Lorraine

Juergen Branke

Warwick Business School

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