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Conference papers

Benchmarking the (1+1)-CMA-ES on the BBOB-2009 Function Testbed

Anne Auger 1, 2 Nikolaus Hansen 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)-CMA-ES is an adaptive stochastic algorithm for the optimization of objective functions defined on a continuous search space in a black-box scenario. In this paper, an independent restart version of the (1+1)-CMA-ES is implemented and benchmarked on the BBOB-2009 noise-free testbed. The maximum number of function evaluations per run is set to $10^{4}$ times the search space dimension. The algorithm solves 23, 13 and 12 of 24 functions in dimension 2, 10 and 40, respectively.
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Submitted on : Sunday, November 8, 2009 - 2:30:21 PM
Last modification on : Friday, January 7, 2022 - 5:48:03 PM
Long-term archiving on: : Thursday, June 17, 2010 - 7:45:07 PM


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  • HAL Id : inria-00430517, version 1



Anne Auger, Nikolaus Hansen. Benchmarking the (1+1)-CMA-ES on the BBOB-2009 Function Testbed. ACM-GECCO Genetic and Evolutionary Computation Conference, Jul 2009, Montreal, Canada. ⟨inria-00430517⟩



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