Performance of Network Crossover on NK Landscapes and Spin Glasses

  • Mark Hauschild
  • Martin Pelikan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6239)

Abstract

This paper describes a network crossover operator based on knowledge gathered from either prior problem-specific knowledge or linkage learning methods such as estimation of distribution algorithms (EDAs). This operator can be used in a genetic algorithm (GA) to incorporate linkage in recombination. The performance of GA with network crossover is compared to that of GA with uniform crossover and the hierarchical Bayesian optimization algorithm (hBOA) on 2D Ising spin glasses, NK landscapes, and SK spin glasses. The results are analyzed and discussed.

Keywords

Genetic Algorithm Execution Time Problem Size Spin Glass Crossover Operator 
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

  • Mark Hauschild
    • 1
  • Martin Pelikan
    • 1
  1. 1.Missouri Estimation of Distribution Algorithms Laboratory, 320 CCBUniversity of Missouri at St. Louis; One University Blvd.St. Louis

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