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

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
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.
Document type :
Conference papers

https://hal.inria.fr/inria-00430516
Contributor : Anne Auger <>
Submitted on : Sunday, November 8, 2009 - 1:54:23 PM
Last modification on : Monday, December 9, 2019 - 5:24:06 PM
Long-term archiving on: Thursday, June 17, 2010 - 7:45:03 PM

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Auger-wk2038.pdf
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• HAL Id : inria-00430516, version 1

### Citation

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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