Enabling High-Dimensional Surrogate-Assisted Optimization by Using Sliding Windows

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2 Citations (Scopus)

Abstract

A major drawback of surrogate-assisted evolutionary algorithms is their limited ability to perform in high-dimensional scenarios. This paper describes a possible meta-algorithm scheme for the application of surrogate models to high-dimensional optimization problems. The main assumption of the proposed method is that for some of these expensive problems the nonlinear interactions between variables are sparse. If these interactions can be represented as a band matrix, they can be exploited by applying low-dimensional heuristic solvers in a sliding window fashion to the high-dimensional problem. A special type of composite test function is presented and the proposed meta-algorithm is compared against standard evolutionary algorithms.
Original languageEnglish
Title of host publicationGECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion
PublisherACM Sigevo
Pages1630-1637
Number of pages8
ISBN (Electronic)9781450349390
ISBN (Print)978-1-4503-4939-0
DOIs
Publication statusPublished - 15 Jul 2017
EventGenetic and Evolutionary Computation Conference (GECCO 2017) - Berlin, Germany, Germany
Duration: 15 Jul 201719 Jul 2017
http://gecco-2017.sigevo.org/

Publication series

NameGECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion

Conference

ConferenceGenetic and Evolutionary Computation Conference (GECCO 2017)
CountryGermany
CityBerlin, Germany
Period15.07.201719.07.2017
Internet address

Keywords

  • High dimensional heuristic optimization
  • Surrogate modeling

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