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Local Optima Networks, Landscape Autocorrelation and Heuristic Search Performance

  • Francisco Chicano
  • Fabio Daolio
  • Gabriela Ochoa
  • Sébastien Vérel
  • Marco Tomassini
  • Enrique Alba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7492)

Abstract

Recent developments in fitness landscape analysis include the study of Local Optima Networks (LON) and applications of the Elementary Landscapes theory. This paper represents a first step at combining these two tools to explore their ability to forecast the performance of search algorithms. We base our analysis on the Quadratic Assignment Problem (QAP) and conduct a large statistical study over 600 generated instances of different types. Our results reveal interesting links between the network measures, the autocorrelation measures and the performance of heuristic search algorithms.

Keywords

Genetic Algorithm Simulated Anneal Local Optimum Landscape Metrics Quadratic Assignment Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Francisco Chicano
    • 1
  • Fabio Daolio
    • 2
  • Gabriela Ochoa
    • 3
  • Sébastien Vérel
    • 4
  • Marco Tomassini
    • 2
  • Enrique Alba
    • 1
  1. 1.E.T.S. Ingeniería InformáticaUniversity of MálagaSpain
  2. 2.Information Systems DepartmentUniversity of LausanneLausanneSwitzerland
  3. 3.Inst. of Computing Sciences and MathematicsUniversity of StirlingScotland, UK
  4. 4.INRIA Lille - Nord Europe and University of Nice Sophia-AntipolisFrance

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