Black-box optimization benchmarking of IPOP-saACM-ES and BIPOP-saACM-ES on the BBOB-2012 noiseless testbed

Ilya Loshchilov 1 Marc Schoenauer 1, 2 Michèle Sebag 1, 3
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 : In this paper, we study the performance of IPOP-saACM-ES and BIPOP-saACM-ES, recently proposed self-adaptive surrogate-assisted Covariance Matrix Adaptation Evolution Strategies. Both algorithms were tested using restarts till a total number of function evaluations of 10^6D was reached, where D is the dimension of the function search space. We compared surrogate-assisted algorithms with their surrogate-less versions IPOP-saACM-ES and BIPOP-saACM-ES, two algorithms with one of the best overall performance observed during the BBOB-2009 and BBOB-2010. The comparison shows that the surrogate-assisted versions outperform the original CMA-ES algorithms by a factor from 2 to 4 on 8 out of 24 noiseless benchmark problems, showing the best results among all algorithms of the BBOB-2009 and BBOB-2010 on Ellipsoid, Discus, Bent Cigar, Sharp Ridge and Sum of different powers functions.
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Communication dans un congrès
Workshop Proceedings of the (GECCO) Genetic and Evolutionary Computation Conference, Jul 2012, Philadelphia, United States. 2012
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Dernière modification le : jeudi 5 avril 2018 - 12:30:12
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  • ARXIV : 1206.5780

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Ilya Loshchilov, Marc Schoenauer, Michèle Sebag. Black-box optimization benchmarking of IPOP-saACM-ES and BIPOP-saACM-ES on the BBOB-2012 noiseless testbed. Workshop Proceedings of the (GECCO) Genetic and Evolutionary Computation Conference, Jul 2012, Philadelphia, United States. 2012. 〈hal-00690444〉

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