On the parallel speed-up of Estimation of Multivariate Normal Algorithm and Evolution Strategies

Fabien Teytaud 1, 2 Olivier Teytaud 3, 4, 5
3 TANC - Algorithmic number theory for cryptology
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], Inria Saclay - Ile de France
5 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : Motivated by parallel optimization, we experiment EDA-like adaptation-rules in the case of $\lambda$ large. The rule we use, essentially based on estimation of multivariate normal algorithm, is (i) compliant with all families of distributions for which a density estimation algorithm exists (ii) simple (iii) parameter-free (iv) better than current rules in this framework of $\lambda$ large. The speed-up as a function of $\lambda$ is consistent with theoretical bounds.
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Submitted on : Saturday, March 21, 2009 - 9:00:41 AM
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Fabien Teytaud, Olivier Teytaud. On the parallel speed-up of Estimation of Multivariate Normal Algorithm and Evolution Strategies. EvoNum (evostar workshop), 2009, Tuebingen, Germany. ⟨inria-00369781⟩

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