Benchmarking the (1+1)-CMA-ES on the BBOB-2009 Noisy 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 : We benchmark an independent-restart-(1+1)-CMA-ES on the BBOB-2009 noisy testbed. 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. The maximum number of function evaluations used here equals $10^{4}$ times the dimension of the search space. The algorithm could only solve $4$ functions with moderate noise in $5$-D and $2$ functions in $20$-D.
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Conference papers
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https://hal.inria.fr/inria-00430518
Contributor : Anne Auger <>
Submitted on : Sunday, November 8, 2009 - 2:33:32 PM
Last modification on : Thursday, April 5, 2018 - 12:30:12 PM
Long-term archiving on : Thursday, June 17, 2010 - 7:45:10 PM

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Anne Auger, Nikolaus Hansen. Benchmarking the (1+1)-CMA-ES on the BBOB-2009 Noisy Testbed. ACM-GECCO Genetic and Evolutionary Computation Conference, Jul 2009, Montreal, Canada. ⟨inria-00430518⟩

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