Instance-based algorithm selection on quadratic assignment problem landscapes.

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7 Citations (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.

Original languageEnglish
Title of host publicationGECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion
PublisherACM Sigevo
Pages1471-1478
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

  • Algorithm selection
  • Feature extraction
  • Fitness landscapes
  • Quadratic assignment problem

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