NDLI: Benchmarking the (1,4)-CMA-ES with mirrored sampling and sequential selection on the noisy BBOB-2010 testbed

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Benchmarking the (1,4)-CMA-ES with mirrored sampling and sequential selection on the noisy BBOB-2010 testbed

Content Provider	ACM Digital Library
Author	Hansen, Nikolaus Auger, Anne Brockhoff, Dimo
Abstract	The Covariance-Matrix-Adaptation Evolution-Strategy (CMA-ES) is a robust stochastic search algorithm for optimizing functions defined on a continuous search space $R^{D}.$ Recently, mirrored samples and sequential selection have been introduced within CMA-ES to improve its local search performances. In this paper, we benchmark the $(1,4_{m}^{s})-CMA-ES$ which implements mirrored samples and sequential selection on the BBOB-2010 noisy testbed. Independent restarts are conducted until a maximal number of $10^{4}$ D function evaluations is reached. Although the tested $(1,4_{m}^{s})-CMA-ES$ is only a local search strategy, it solves 8 of the noisy BBOB-2010 functions in 20D and 9 of them in 5D for a target of $10^{-8}.$ There is also one additional function in 20D and 5 additional functions in 5D where a successful run for at least one of the 15 instances can be reported. Moreover, on 7 of the 8 functions that are solved by the $(1,4_{m}^{s})-CMA-ES$ in 20D, we see a large improvement over the best algorithm of the BBOB-2009 benchmarking for the corresponding functions--ranging from an 37% improvement on the sphere with moderate Cauchy noise to a speed-up by a factor of about 3 on the Gallagher function with Cauchy noise.
Starting Page	1625
Ending Page	1632
Page Count	8
File Format	PDF
ISBN	9781450300735
DOI	10.1145/1830761.1830782
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-07-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Black-box optimization Benchmarking
Content Type	Text
Resource Type	Article

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