Black-Box Optimization Benchmarking of NEWUOA compared to BIPOP-CMA-ES

Raymond Ros 1 Nikolaus Hansen 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 this paper, the performances of the NEW Unconstrained Optimization Algorithm (NEWUOA) on some noiseless functions are compared to those of the BI-POPulation Covariance Matrix Adaptation-Evolution Strategy (BIPOP-CMA-ES). The two algorithms were benchmarked on the BBOB 2009 noiseless function testbed. The comparison shows that NEWUOA outperforms BIPOP-CMA-ES on some functions like the Sphere or the Rosenbrock functions. Also the independent restart procedure used for NEWUOA allows it to perform better than BIPOP-CMA-ES on the Gallagher functions. Nevertheless, BIPOP-CMA-ES is faster and has a better success probability than NEWUOA in reaching target function values smaller than one on all other functions.
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Communication dans un congrès
Genetic and Evolutionary Computation Conference 2010, Jul 2010, Portland, OR, United States. 2010
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https://hal.inria.fr/inria-00473779
Contributeur : Raymond Ros <>
Soumis le : vendredi 16 avril 2010 - 16:58:49
Dernière modification le : jeudi 5 avril 2018 - 12:30:12
Document(s) archivé(s) le : mardi 28 septembre 2010 - 11:46:18

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Raymond Ros, Nikolaus Hansen. Black-Box Optimization Benchmarking of NEWUOA compared to BIPOP-CMA-ES. Genetic and Evolutionary Computation Conference 2010, Jul 2010, Portland, OR, United States. 2010. 〈inria-00473779〉

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