Mirrored Variants of the (1,2)-CMA-ES Compared on the Noiseless BBOB-2010 Testbed

Anne Auger 1 Dimo Brockhoff 1, * Nikolaus Hansen 1
* Corresponding author
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 : Derandomization by means of mirroring has been recently introduced to enhance the performances of $(1,\lambda)$-Evolution-Strategies (ESs) with the aim of designing fast robust local search stochastic algorithms. This paper compares on the BBOB-2010 noiseless benchmark testbed two variants of the (1,2)-CMA-ES where the mirroring method is implemented. Independent restarts are conducted till a total budget of $10^{4} D$ function evaluations per trial is reached, where $D$ is the dimension of the search space. The results show that the improved variants increase the success probability on 5 (respectively 7) out of 24 test functions in 20D and at the same time are significantly faster on 9 (10) functions in 20D by a factor of about 2--3 (2--4) for a target value of $10^{-7}$ while in no case, the baseline (1,2)-CMA-ES is significantly faster on any tested target function value in 5D and 20D.
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Conference papers
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Submitted on : Wednesday, July 14, 2010 - 10:13:03 PM
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Anne Auger, Dimo Brockhoff, Nikolaus Hansen. Mirrored Variants of the (1,2)-CMA-ES Compared on the Noiseless BBOB-2010 Testbed. GECCO workshop on Black-Box Optimization Benchmarking (BBOB'2010), Jul 2010, Portland, OR, United States. pp.1551-1558, ⟨10.1145/1830761.1830772⟩. ⟨inria-00502435⟩



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