Linear Convergence of Evolution Strategies with Derandomized Sampling Beyond Quasi-Convex Functions

Jérémie Decock 1, 2 Olivier Teytaud 1, 2
1 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
2 LRI - Laboratoire de Recherche en Informatique
Abstract : We study the linear convergence of a simple evolutionary algorithm on non quasi-convex functions on continuous domains. Assumptions include an assumption on the sampling performed by the evolutionary algorithm (supposed to cover efficiently the neighborhood of the current search point), the conditioning of the objective function (so that the probability of improvement is not too low at each time step, given a correct step size), and the unicity of the optimum.
Mots-clés : optimization linear convergence evolution strategies non quasi-convex functions
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Conference papers
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Submitted on : Thursday, November 21, 2013 - 3:45:47 PM
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Jérémie Decock, Olivier Teytaud. Linear Convergence of Evolution Strategies with Derandomized Sampling Beyond Quasi-Convex Functions. EA - 11th Biennal International Conference on Artificial Evolution - 2013, Oct 2013, Bordeaux, France. ⟨hal-00907671⟩

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