A Multicriteria Generalization of Bayesian Global Optimization

Michael Emmerich (), Kaifeng Yang (), André Deutz (), Hao Wang () and Carlos M. Fonseca ()
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Michael Emmerich: Leiden University
Kaifeng Yang: Leiden University
André Deutz: Leiden University
Hao Wang: Leiden University
Carlos M. Fonseca: University of Coimbra

A chapter in Advances in Stochastic and Deterministic Global Optimization, 2016, pp 229-242 from Springer

Abstract: 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.

Keywords: Bayesian Global Optimization; Expected Hypervolume Improvement; Computation Complexity (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/978-3-319-29975-4_12

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