BI-population CMA-ES Algorithms with Surrogate Models and Line Searches

Ilya Loshchilov 1 Marc Schoenauer 2, 3 Michèle Sebag 3
1 Laboratory of Intelligent Systems (LIS)
LIS - Laboratory of Intelligent Systems
2 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, three extensions of the BI-population Covariance Matrix Adaptation Evolution Strategy with weighted active covariance matrix update (BIPOP-aCMA-ES) are investigated. First, to address expensive optimization, we benchmark a recently proposed extension of the self-adaptive surrogate-assisted CMA-ES which benefits from more intensive surrogate model exploitation (BIPOP-saACM-k). Second, to address separable optimization, we propose a hybrid of BIPOP-aCMA-ES and STEP algorithm with coordinate-wise line search (BIPOP-aCMA-STEP). Third, we propose HCMA, a hybrid of BIPOP-saACM-k, STEP and NEWUOA to benefit both from surrogate models and line searches. All algorithms were tested on the noiseless BBOB testbed using restarts till a total number of function evaluations of $10^6n$ was reached, where $n$ is the dimension of the function search space. The comparison shows that BIPOP-saACM-k outperforms its predecessor BIPOP-saACM up to a factor of 2 on ill-conditioned problems, while BIPOP-aCMA-STEP outperforms the original BIPOP-based algorithms on separable functions. The hybrid HCMA algorithm demonstrates the best overall performance compared to the best algorithms of the BBOB-2009, BBOB-2010 and BBOB-2012 when running for more than $100n$ function evaluations.
Type de document :
Communication dans un congrès
Workshop Proceedings of the (GECCO) Genetic and Evolutionary Computation Conference, Jul 2013, Amsterdam, Netherlands. ACM, pp.8, 2013, Workshop Proceedings of the (GECCO) Genetic and Evolutionary Computation Conference
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Contributeur : Loshchilov Ilya <>
Soumis le : samedi 4 mai 2013 - 11:45:36
Dernière modification le : jeudi 5 avril 2018 - 12:30:12
Document(s) archivé(s) le : lundi 5 août 2013 - 04:03:03

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Ilya Loshchilov, Marc Schoenauer, Michèle Sebag. BI-population CMA-ES Algorithms with Surrogate Models and Line Searches. Workshop Proceedings of the (GECCO) Genetic and Evolutionary Computation Conference, Jul 2013, Amsterdam, Netherlands. ACM, pp.8, 2013, Workshop Proceedings of the (GECCO) Genetic and Evolutionary Computation Conference. 〈hal-00818596v2〉

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