Automatic algorithm selection for the quadratic assignment problem using fitness landscape analysis

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

9 Citations (Scopus)

Abstract

In the last few years, fitness landscape analysis has seen an increase in interest due to the availability of large problem collections and research groups focusing on the development of a wide array of different optimization algorithms for diverse tasks. Instead of being able to rely on a single trusted method that is tuned and tweaked to the application more and more, new problems are investigated, where little or no experience has been collected. In an attempt to provide a more general criterion for algorithm and parameter selection other than "it works better than something else we tried", sophisticated problem analysis and classification schemes are employed. In this work, we combine several of these analysis methods and evaluate the suitability of fitness landscape analysis for the task of algorithm selection.

Original languageEnglish
Title of host publicationEvolutionary Computation in Combinatorial Optimization - 13th European Conference, EvoCOP 2013, Proceedings
PublisherSpringer
Pages109-120
Number of pages12
ISBN (Print)9783642371974
DOIs
Publication statusPublished - 2013
Event13th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2013 - Vienna, Austria
Duration: 3 Apr 20135 Apr 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7832 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2013
CountryAustria
CityVienna
Period03.04.201305.04.2013

Keywords

  • Fitness Landscape Analysis
  • Problem Understanding
  • Quadratic Assignment Problem
  • Robust Taboo Search
  • Variable Neighborhood Search

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