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Title: First-Improvement vs. Best-Improvement Local Optima Networks of NK Landscapes
Authors: Ochoa, Gabriela
Verel, Sebastien
Tomassini, Marco
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Editors: Schaefer, R
Cotta, C
Kolodziej, J
Rudolph, G
Citation: Ochoa G, Verel S & Tomassini M (2010) First-Improvement vs. Best-Improvement Local Optima Networks of NK Landscapes. In: Schaefer R, Cotta C, Kolodziej J, Rudolph G (ed.). Parallel Problem Solving from Nature, PPSN XI: 11th International Conference, Kraków, Poland, September 11-15, 2010, Proceedings, Part I. Lecture Notes in Computer Science, 6238, Berlin Heidelberg: Springer, pp. 104-113.
Issue Date: 2010
Publisher: Springer
Series/Report no.: Lecture Notes in Computer Science, 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.
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Type: Part of book or chapter of book
Affiliation: Computing Science - CSM Dept
University of Nice Sophia-Antipolis, France
University of Lausanne, Switzerland

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