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Bayesian Optimization Approaches for Massively Multi-modal Problems

descriptionPublicationkeyboard_double_arrow_right Part of book or chapter of book , Article 01 Jan 2020 Spain Publisher:Springer International Publishing

Authors: Ibai Roman; Alexander Mendiburu; Roberto Santana; Jose A. Lozano;

doi: 10.1007/978-3-030-38629-0_31

handle: 20.500.11824/1135

Bayesian Optimization Approaches for Massively Multi-modal Problems

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Abstract

The optimization of massively multi-modal functions is a challenging task, particularly for problems where the search space can lead the op- timization process to local optima. While evolutionary algorithms have been extensively investigated for these optimization problems, Bayesian Optimization algorithms have not been explored to the same extent. In this paper, we study the behavior of Bayesian Optimization as part of a hybrid approach for solving several massively multi-modal functions. We use well-known benchmarks and metrics to evaluate how different variants of Bayesian Optimization deal with multi-modality.

TIN2016-78365-R

Country

Spain

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Keywords

Optimization, multi-modal problems, Bayesian

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