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DCMA, yet another derandomization in Covariance-Matrix-Adaptation

By Olivier Teytaud and Sylvain Gelly


In a preliminary part of this paper, we analyze the necessity of randomness in evolution strategies. We conclude to the necessity of ”continuous”-randomness, but with a restricted amount of random seeds, and apply this results to CMA-ES, a famous evolution strategy already based on the idea of derandomization, which uses random independent gaussian mutations. We here replace these random independent gaussian mutations by a quasi-random sample. The modification is very easy, computationally faster and the convergence is faster

Year: 2011
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