Algorithm selection on generalized quadratic assignment problem landscapes

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3 Citations (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.

Original languageEnglish
Title of host publicationGECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery, Inc
Pages253-260
Number of pages8
ISBN (Electronic)9781450356183
DOIs
Publication statusPublished - 2 Jul 2018
Event2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan
Duration: 15 Jul 201819 Jul 2018

Publication series

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

Conference

Conference2018 Genetic and Evolutionary Computation Conference, GECCO 2018
CountryJapan
CityKyoto
Period15.07.201819.07.2018

Keywords

  • Algorithm selection
  • Assignment problems
  • Fitness landscapes

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