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Benchmarking Large Scale Variants of CMA-ES and L-BFGS-B on the bbob-largescale Testbed

Konstantinos Varelas 1, 2
1 RANDOPT - Randomized Optimisation
CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique, Inria Saclay - Ile de France
Abstract : In this paper we benchmark five variants of CMA-ES for optimization in large dimension on the novel large scale testbed of COCO under default or modified parameter settings. In particular, we compare the performance of the separable CMA-ES, of VD-CMA-ES and VkD-CMA-ES, of two implementations of the Limited Memory CMA-ES and of the Rank m Evolution Strategy, RmES. For VkD-CMA-ES we perform experiments with different complexity models of the search distribution and for RmES we study the impact of the number of evolution paths employed by the algorithm. The quasi-Newton L-BFGS-B algorithm is also benchmarked and we investigate the effect of choosing the maximum number of variable metric corrections for the Hessian approximation. As baseline comparison, we provide results of CMA-ES up to dimension 320.
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Submitted on : Wednesday, June 19, 2019 - 12:42:46 PM
Last modification on : Thursday, May 20, 2021 - 9:06:02 AM

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Konstantinos Varelas. Benchmarking Large Scale Variants of CMA-ES and L-BFGS-B on the bbob-largescale Testbed. GECCO 2019 - The Genetic and Evolutionary Computation Conference, Jul 2019, Prague, Czech Republic. ⟨10.1145/3319619.3326893⟩. ⟨hal-02160106⟩

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