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inria-00173207, version 1

DCMA, yet another derandomization in covariance-matrix-adaptation

Olivier Teytaud () 1, Sylvain Gelly 1

GECCO (2007)

Abstract: In a preliminary part of this paper, we analyze the neces- sity of randomness in evolution strategies. We conclude to the necessity of ”continuous”-randomness, but with a much more limited use of randomness than what is commonly used in evolution strategies. We then apply these results to CMA- ES, a famous evolution strategy already based on the idea of derandomization, which uses random independent Gaus- sian mutations. We here replace these random independent Gaussian mutations by a quasi-random sample. The mod- ification is very easy to do, the modified algorithm is com- putationally more efficient and its convergence is faster in terms of the number of iterates for a given precision.

  • 1:  TAO (INRIA Futurs)
  • INRIA – CNRS : UMR8623 – Université Paris XI - Paris Sud
  • Domain : Mathematics/Optimization and Control
  • Keywords : Derandomization – Evolutionary Algorithms – Low-discrepancy – Quasi-Random
 
  • inria-00173207, version 1
  • oai:hal.inria.fr:inria-00173207
  • From: 
  • Submitted on: Wednesday, 19 September 2007 15:46:02
  • Updated on: Wednesday, 19 September 2007 19:03:20
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