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Benchmarking the (1+1)-ES with One-Fifth Success rule on the BBOB-2009 Noisy Testbed

Anne Auger 1, 2
1 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 : The (1+1)-ES with one-fifth success rule is one of the first and simplest stochastic algorithm proposed for optimization on a continuous search space in a black-box scenario. In this paper, we benchmark an independent-restart (1+1)-ES with one-fifth success rule on the BBOB-2009 noisy testbed. The maximum number of function evaluations used equals $10^{6}$ times the dimension of the search space. The algorithm could only solve 3 functions with moderate noise in 5-D and 2 functions in 20-D.
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Submitted on : Sunday, November 8, 2009 - 1:54:23 PM
Last modification on : Thursday, July 8, 2021 - 3:48:17 AM
Long-term archiving on: : Thursday, June 17, 2010 - 7:45:03 PM


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  • HAL Id : inria-00430516, version 1



Anne Auger. Benchmarking the (1+1)-ES with One-Fifth Success rule on the BBOB-2009 Noisy Testbed. ACM-GECCO Genetic and Evolutionary Computation Conference, Jul 2009, Montreal, Canada. ⟨inria-00430516⟩



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