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GECCO
2010
Springer

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

9 years 3 months ago
Benchmarking the (1, 4)-CMA-ES with mirrored sampling and sequential selection on the noisy BBOB-2010 testbed
The Covariance-Matrix-Adaptation Evolution-Strategy (CMA-ES) is a robust stochastic search algorithm for optimizing functions defined on a continuous search space RD . Recently, mirrored samples and sequential selection have been introduced within CMA-ES to improve its local search performances. In this paper, we benchmark the (1,4s m)CMA-ES which implements mirrored samples and sequential selection on the BBOB-2010 noisy testbed. Independent restarts are conducted until a maximal number of 104 D function evaluations is reached. Although the tested (1,4s m)-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 (1,4s m)-CMA-ES in 20D, we see a large improvement over the best algorithm of the BBO...
Anne Auger, Dimo Brockhoff, Nikolaus Hansen
Added 12 Aug 2010
Updated 12 Aug 2010
Type Conference
Year 2010
Where GECCO
Authors Anne Auger, Dimo Brockhoff, Nikolaus Hansen
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