Towards single-And multiobjective Bayesian global optimization for mixed integer problems

Kaifeng Yang, Koen Van Der Blom, Thomas Bäck, Michael Emmerich

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

7 Citations (Scopus)

Abstract

Bayesian Global Optimization (BGO) is a very efficient technique to optimize expensive evaluation problems. However, the application domain is limited to continuous search spaces when using a BGO algorithm. To solve mixed integer problems with a BGO algorithm, this paper adapts the heterogeneous distance function to construct the Kriging models and applies these new Kriging models in Multi-objective Bayesian Global Optimization (MOBGO). The proposed mixed integer MOBGO algorithm and the traditional MOBGO algorithm are compared on three mixed integer multi-objective optimization problems (MOP), w.r.t. The mean value of the hypervolume (HV) and the related standard deviation.

Original languageEnglish
Title of host publicationProceedings LeGO 2018 � 14th International Global Optimization Workshop
EditorsAndre H. Deutz, Sander C. Hille, Yaroslav D. Sergeyev, Michael T. M. Emmerich
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735417984
DOIs
Publication statusPublished - 12 Feb 2019
Event14th International Global Optimization Workshop, LeGO 2018 - Leiden, Netherlands
Duration: 18 Sept 201821 Sept 2018

Publication series

NameAIP Conference Proceedings
Volume2070
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference14th International Global Optimization Workshop, LeGO 2018
Country/TerritoryNetherlands
CityLeiden
Period18.09.201821.09.2018

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