Asynchronous surrogate-assisted optimization networks

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

Abstract

This paper introduces a new, highly asynchronous method for surrogate-assisted optimization where it is possible to concurrently create surrogate models, evaluate fitness functions and do parameter optimization for the underlying problem, effectively eliminating sequential workflows of other surrogate-assisted algorithms. Using optimization networks, each part of the optimization process is exchangeable. First experiments are done for single objective benchmark functions, namely Ackley, Griewank, Schwefel and Rastrigin, using problem sizes starting from 2D up to 10D, and other EGO implementations are used as baseline for comparison. First results show that the implemented network approach is competitive to other EGO implementations in terms of achieved solution qualities and more efficient in terms of execution times.

OriginalspracheEnglisch
TitelGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
Herausgeber (Verlag)Association for Computing Machinery, Inc
Seiten1266-1267
Seitenumfang2
ISBN (elektronisch)9781450357647
DOIs
PublikationsstatusVeröffentlicht - 6 Jul 2018
Veranstaltung2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan
Dauer: 15 Jul 201819 Jul 2018

Publikationsreihe

NameGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion

Konferenz

Konferenz2018 Genetic and Evolutionary Computation Conference, GECCO 2018
LandJapan
OrtKyoto
Zeitraum15.07.201819.07.2018

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