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Sexual Recombination in Self-Organizing Interaction Networks

  • Joshua L. Payne
  • Jason H. Moore
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)

Abstract

We build on recent advances in the design of self-organizing interaction networks by introducing a sexual variant of an existing asexual, mutation-limited algorithm. Both the asexual and sexual variants are tested on benchmark optimization problems with varying levels of problem difficulty, deception, and epistasis. Specifically, we investigate algorithm performance on Massively Multimodal Deceptive Problems and NK Landscapes. In the former case, we find that sexual recombination improves solution quality for all problem instances considered; in the latter case, sexual recombination is not found to offer any significant improvement. We conclude that sexual recombination in self-organizing interaction networks may improve solution quality in problem domains with deception, and discuss directions for future research.

Keywords

Interaction Network Problem Instance Sexual Case Complex Adaptive System Multiobjective Optimization 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 2010

Authors and Affiliations

  • Joshua L. Payne
    • 1
  • Jason H. Moore
    • 1
  1. 1.Computational Genetics LaboratoryDartmouth Medical SchoolLebanonUSA

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