Instance-based algorithm selection on quadratic assignment problem landscapes.

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

9 Zitate (Scopus)

Abstract

Among the many applications of fitness landscape analysis a prominent example is algorithm selection.The no-free-lunch (NFL) theorem has put a limit on the general applicability of heuristic search methods. Improved methods can only be found by specialization to certain problem characteristics which limits their application to other problems. .is creates a very interesting and dynamic field for algorithm development. However, this also leads to the definition of a large range of di.erent algorithms that are hard to compare exhaustively. An additional challenge is posed by the fact that algorithms have parameters and thus to each algorithm there may be a large number of instances. In this work the application of algorithm selection to problem instances of the quadratic assignment problem (QAP) is discussed.

OriginalspracheEnglisch
TitelGECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion
Herausgeber (Verlag)ACM Sigevo
Seiten1471-1478
Seitenumfang8
ISBN (elektronisch)9781450349390
ISBN (Print)978-1-4503-4939-0
DOIs
PublikationsstatusVeröffentlicht - 15 Jul 2017
VeranstaltungGenetic and Evolutionary Computation Conference (GECCO 2017) - Berlin, Germany, Deutschland
Dauer: 15 Jul 201719 Jul 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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