Algorithm selection on generalized quadratic assignment problem landscapes

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

4 Zitate (Scopus)

Abstract

Algorithm selection is useful in decision situations where among many alternative algorithm instances one has to be chosen. This is often the case in heuristic optimization and is detailed by the well-known no-free-lunch (NFL) theorem. A consequence of the NFL is that a heuristic algorithm may only gain a performance improvement in a subset of the problems. With the present study we aim to identify correlations between observed differences in performance and problem characteristics obtained from statistical analysis of the problem instance and from fitness landscape analysis (FLA). Finally, we evaluate the performance of a recommendation algorithm that uses this information to make an informed choice for a certain algorithm instance.

OriginalspracheEnglisch
TitelGECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference
Herausgeber (Verlag)Association for Computing Machinery, Inc
Seiten253-260
Seitenumfang8
ISBN (elektronisch)9781450356183
DOIs
PublikationsstatusVeröffentlicht - 2 Jul 2018
Veranstaltung2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan
Dauer: 15 Jul 201819 Jul 2018

Publikationsreihe

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

Konferenz

Konferenz2018 Genetic and Evolutionary Computation Conference, GECCO 2018
Land/GebietJapan
OrtKyoto
Zeitraum15.07.201819.07.2018

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