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A Gender-Based Genetic Algorithm for the Automatic Configuration of Algorithms

  • Carlos Ansótegui
  • Meinolf Sellmann
  • Kevin Tierney
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5732)

Abstract

A problem that is inherent to the development and efficient use of solvers is that of tuning parameters. The CP community has a long history of addressing this task automatically. We propose a robust, inherently parallel genetic algorithm for the problem of configuring solvers automatically. In order to cope with the high costs of evaluating the fitness of individuals, we introduce a gender separation whereby we apply different selection pressure on both genders. Experimental results on a selection of SAT solvers show significant performance and robustness gains over the current state-of-the-art in automatic algorithm configuration.

Keywords

Genetic Algorithm Training Instance Iterate Local Search Parallel Genetic Algorithm Linear Genetic Program 
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 2009

Authors and Affiliations

  • Carlos Ansótegui
    • 1
  • Meinolf Sellmann
    • 2
  • Kevin Tierney
    • 2
  1. 1.Universitat de LleidaSpain
  2. 2.Department of Computer ScienceBrown UniversityProvidenceUSA

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