Open AccessBenchmarking large scale variants of CMA-ES and L-BFGS-B on the bbob-largescale testbed
Author(s) -
Konstantinos Varelas
Publication year - 2019
Publication title -
proceedings of the genetic and evolutionary computation conference companion
Language(s) - English
Resource type - Conference proceedings
ISBN - 978-1-4503-6748-6
DOI - 10.1145/3319619.3326893
Subject(s) - broyden–fletcher–goldfarb–shanno algorithm , testbed , benchmark (surveying) , dimension (graph theory) , metric (unit) , separable space , hessian matrix , computer science , benchmarking , mathematical optimization , rank (graph theory) , cma es , scale (ratio) , algorithm , mathematics , evolutionary computation , evolution strategy , combinatorics , engineering , computer network , mathematical analysis , operations management , physics , asynchronous communication , geodesy , marketing , quantum mechanics , business , geography
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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