Evolution Strategies with Additive Noise: A Convergence Rate Lower BoundReport as inadecuate




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1 LRI - Laboratoire de Recherche en Informatique 2 TAO - Machine Learning and Optimisation LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623

Abstract : We consider the problem of optimizing functions corrupted with additive noise. It is known that evolutionary algo-rithms can reach a simple regret O1- √ n within logarith-mic factors, when n is the number of function evaluations. We show mathematically that this bound is tight, at least for a wide family of evolution strategies without large mutations.

Keywords : Evolution Strategies Noisy Optimization Additive Noise





Author: Sandra Astete-Morales - Marie-Liesse Cauwet - Olivier Teytaud -

Source: https://hal.archives-ouvertes.fr/



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