## 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.

Originalsprache | Englisch |
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Titel | GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion |

Herausgeber (Verlag) | ACM Sigevo |

Seiten | 1471-1478 |

Seitenumfang | 8 |

ISBN (elektronisch) | 9781450349390 |

ISBN (Print) | 978-1-4503-4939-0 |

DOIs | |

Publikationsstatus | Veröffentlicht - 15 Juli 2017 |

Veranstaltung | Genetic and Evolutionary Computation Conference (GECCO 2017) - Berlin, Germany, Deutschland Dauer: 15 Juli 2017 → 19 Juli 2017 http://gecco-2017.sigevo.org/ |

### Publikationsreihe

Name | GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion |
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### Konferenz

Konferenz | Genetic and Evolutionary Computation Conference (GECCO 2017) |
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Land/Gebiet | Deutschland |

Ort | Berlin, Germany |

Zeitraum | 15.07.2017 → 19.07.2017 |

Internetadresse |