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First-Improvement vs. Best-Improvement Local Optima Networks of NK Landscapes

  • Gabriela Ochoa
  • Sébastien Verel
  • Marco Tomassini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6238)

Abstract

This paper extends a recently proposed model for combinatorial landscapes: Local Optima Networks (LON), to incorporate a first-improvement (greedy-ascent) hill-climbing algorithm, instead of a best-improvement (steepest-ascent) one, for the definition and extraction of the basins of attraction of the landscape optima. A statistical analysis comparing best and first improvement network models for a set of NK landscapes, is presented and discussed. Our results suggest structural differences between the two models with respect to both the network connectivity, and the nature of the basins of attraction. The impact of these differences in the behavior of search heuristics based on first and best improvement local search is thoroughly discussed.

Keywords

Local Search Local Optimum Local Search Algorithm Short Path Length Stochastic Local Search 
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 2010

Authors and Affiliations

  • Gabriela Ochoa
    • 1
  • Sébastien Verel
    • 2
  • Marco Tomassini
    • 3
  1. 1.School of Computer ScienceUniversity of NottinghamNottinghamUK
  2. 2.INRIA Lille - Nord Europe and University of Nice Sophia-AntipolisFrance
  3. 3.Information Systems DepartmentUniversity of LausanneLausanneSwitzerland

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