Benchmarking a Weighted Negative Covariance Matrix Update on the BBOB-2010 Noisy Testbed

Nikolaus Hansen 1 Raymond Ros 1
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 : In a companion paper, we presented a weighted negative update of the covariance matrix in the CMA-ES—weighted active CMA-ES or, in short, aCMA-ES. In this paper, we benchmark the IPOP-aCMA-ES on the BBOB-2010 noisy testbed in search space dimension between 2 and 40 and compare its performance with the IPOP-CMA-ES. The aCMA suffers from a moderate performance loss, of less than a factor of two, on the sphere function with two different noise models. On the other hand, the aCMA enjoys a (significant) performance gain, up to a factor of four, on 13 unimodal functions in various dimensions, in particular the larger ones. Compared to the best performance observed during BBOB-2009, the IPOP-aCMA-ES sets a new record on overall ten functions. The global picture is in favor of aCMA which might establish a new standard also for noisy problems.
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Communication dans un congrès
Genetic And Evolutionary Computation Conference, Jul 2010, Portland, United States
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Contributeur : Nikolaus Hansen <>
Soumis le : samedi 11 décembre 2010 - 16:03:29
Dernière modification le : jeudi 5 avril 2018 - 12:30:12
Document(s) archivé(s) le : samedi 12 mars 2011 - 02:38:14

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Nikolaus Hansen, Raymond Ros. Benchmarking a Weighted Negative Covariance Matrix Update on the BBOB-2010 Noisy Testbed. Genetic And Evolutionary Computation Conference, Jul 2010, Portland, United States. 〈hal-00545729〉

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