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2016 | OriginalPaper | Buchkapitel

A Multicriteria Generalization of Bayesian Global Optimization

verfasst von : Michael Emmerich, Kaifeng Yang, André Deutz, Hao Wang, Carlos M. Fonseca

Erschienen in: Advances in Stochastic and Deterministic Global Optimization

Verlag: Springer International Publishing

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Abstract

This chapter discusses a generalization of the expected improvement used in Bayesian global optimization to the multicriteria optimization domain, where the goal is to find an approximation to the Pareto front. The expected hypervolume improvement (EHVI) measures improvement as the gain in dominated hypervolume relative to a given approximation to the Pareto front. We will review known properties of the EHVI, applications in practice and propose a new exact algorithm for computing EHVI. The new algorithm has asymptotically optimal time complexity O(nlogn). This improves existing computation schemes by a factor of n∕logn. It shows that this measure, at least for a small number of objective functions, is as fast as other simpler measures of multicriteria expected improvement that were considered in recent years.

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Vorheriges Kapitel On the Least-Squares Fitting of Data by Sinusoids

Nächstes Kapitel Understanding the Impact of Constraints: A Rank Based Fitness Function for Evolutionary Methods

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Titel: A Multicriteria Generalization of Bayesian Global Optimization
verfasst von: Michael Emmerich
Kaifeng Yang
André Deutz
Hao Wang
Carlos M. Fonseca
Verlag: Springer International Publishing
Buch: Advances in Stochastic and Deterministic Global Optimization
Print ISBN: 978-3-319-29973-0

Electronic ISBN: 978-3-319-29975-4

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-29975-4_12

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