Efficient global optimization: Motivation, variations and applications

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dc.contributor.authorWeihs, Claus
dc.contributor.authorHerbrandt, Swetlana
dc.contributor.authorBauer, Nadja
dc.contributor.authorFriedrichs, Klaus
dc.contributor.authorHorn, Daniel
dc.date.accessioned2016-11-08T09:15:59Z
dc.date.available2016-11-08T09:15:59Z
dc.date.issued2016
dc.description.abstractA popular optimization method of a black box objective function is Efficient Global Optimization (EGO), also known as Sequential Model Based Optimization, SMBO, with kriging and expected improvement. EGO is a sequential design of experiments aiming at gaining as much information as possible from as few experiments as feasible by a skillful choice of the factor settings in a sequential way. In this paper we will introduce the standard procedure and some of its variants. In particular, we will propose some new variants like regression as a modeling alternative to kriging and two simple methods for the handling of categorical variables, and we will discuss focus search for the optimization of the infill criterion. Finally, we will give relevant examples for the application of the method. Moreover, in our group, we implemented all the described methods in the publicly available R package mlrMBO.en
dc.identifier.urihttp://hdl.handle.net/2003/35315
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-17358
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;64, 2016en
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleEfficient global optimization: Motivation, variations and applicationsen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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Sonderforschungsbereich (SFB) 823