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

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

4 Zitate (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.
OriginalspracheEnglisch
TitelGECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion
Herausgeber (Verlag)ACM Sigevo
Seiten1630-1637
Seitenumfang8
ISBN (elektronisch)9781450349390
ISBN (Print)978-1-4503-4939-0
DOIs
PublikationsstatusVeröffentlicht - 15 Juli 2017
VeranstaltungGenetic and Evolutionary Computation Conference (GECCO 2017) - Berlin, Germany, Deutschland
Dauer: 15 Juli 201719 Juli 2017
http://gecco-2017.sigevo.org/

Publikationsreihe

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

Konferenz

KonferenzGenetic and Evolutionary Computation Conference (GECCO 2017)
Land/GebietDeutschland
OrtBerlin, Germany
Zeitraum15.07.201719.07.2017
Internetadresse

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