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2020 | OriginalPaper | Buchkapitel

Bayesian Optimization Approaches for Massively Multi-modal Problems

verfasst von : Ibai Roman, Alexander Mendiburu, Roberto Santana, Jose A. Lozano

Erschienen in: Learning and Intelligent Optimization

Verlag: Springer International Publishing

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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 optimization 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.

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Titel: Bayesian Optimization Approaches for Massively Multi-modal Problems
verfasst von: Ibai Roman
Alexander Mendiburu
Roberto Santana
Jose A. Lozano
Verlag: Springer International Publishing
Buch: Learning and Intelligent Optimization
Print ISBN: 978-3-030-38628-3

Electronic ISBN: 978-3-030-38629-0

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-38629-0_31

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