inria-00173207, version 1
DCMA, yet another derandomization in covariance-matrix-adaptation
Olivier Teytaud
1Sylvain 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
- http://hal.inria.fr/inria-00173207
- oai:hal.inria.fr:inria-00173207
- From: Olivier Teytaud
- Submitted on: Wednesday, 19 September 2007 15:46:02
- Updated on: Wednesday, 19 September 2007 19:03:20






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