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A New Selection Ratio for Large Population Sizes

  • Fabien Teytaud
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)

Abstract

Motivated by parallel optimization, we study the Self-Adaptation algorithm for large population sizes. We first show that the current version of this algorithm does not reach the theoretical bounds, then we propose a very simple modification, in the selection part of the evolution process. We show that this simple modification leads to big improvement of the speed-up when the population size is large.

Keywords

Convergence Rate Evolution Strategy Sphere Function Selection Ratio Adaptation Algorithm 
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

  • Fabien Teytaud
    • 1
  1. 1.TAO (Inria), LRI, UMR 8623 (CNRS - Univ. Paris-Sud)OrsayFrance

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