Progress Rate in Noisy Genetic Programming for Choosing λ

Jean-Baptiste Hoock 1, 2 Olivier Teytaud 1, 2, 3
2 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 : Recently, it has been proposed to use Bernstein races for implementing non-regression testing in noisy genetic programming. We study the population size of such a (1+λ) evolutionary algorithm applied to a noisy fitness function optimization by a progress rate analysis and experiment it on a policy search application.
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Jean-Baptiste Hoock, Olivier Teytaud. Progress Rate in Noisy Genetic Programming for Choosing λ. Artificial Evolution, Oct 2011, Angers, France. ⟨inria-00622150⟩

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