Investigating the Impact of Sequential Selection in the (1,2)-CMA-ES on the Noiseless BBOB-2010 Testbed

Anne Auger 1 Dimo Brockhoff 1, * Nikolaus Hansen 1
* Corresponding author
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 : This paper investigates the impact of sequential selection, a concept recently introduced for Evolution Strategies (ESs), that consists in performing the evaluations of the different candidate solutions sequentially, concluding the iteration immediately if one offspring is better than the parent. The performance of the (1,2)-Covariance-Matrix-Adaptation Evolution-Strategy (CMA-ES) is compared to the performance of the (1,2$^s$)-CMA-ES where sequential selection is implemented on the BBOB-2010 noiseless benchmark testbed. For each strategy, an independent restart mechanism is implemented. A total budget of $10^{4} D$ function evaluations per trial has been used, where $D$ is the dimension of the search space. The experiments do not allow a general statement regarding a statistically significant difference between the two algorithms and we conclude that the sequential selection has no impact on the performance of the (1,2)-CMA-ES on the noiseless BBOB-2009 testbed.
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Conference papers
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https://hal.inria.fr/inria-00502431
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Submitted on : Wednesday, July 14, 2010 - 9:26:43 PM
Last modification on : Thursday, April 5, 2018 - 12:30:12 PM
Long-term archiving on : Friday, October 15, 2010 - 3:28:11 PM

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Anne Auger, Dimo Brockhoff, Nikolaus Hansen. Investigating the Impact of Sequential Selection in the (1,2)-CMA-ES on the Noiseless BBOB-2010 Testbed. GECCO workshop on Black-Box Optimization Benchmarking (BBOB'2010), Jul 2010, Portland, OR, United States. pp.1591-1596, ⟨10.1145/1830761.1830777⟩. ⟨inria-00502431⟩

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