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GECCO
2010
Springer

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

9 years 3 months ago
Benchmarking the (1, 4)-CMA-ES with mirrored sampling and sequential selection on the noiseless BBOB-2010 testbed
The well-known 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 noiseless testbed. Independent restarts are conducted until a maximal number of 104 D function evaluations is reached. The experiments show that 11 of the 24 functions are solved in 20D (and 13 in 5D respectively). Compared to the function-wise target-wise best algorithm of the BBOB-2009 benchmarking, on 25% of the functions the (1,4s m)-CMA-ES is at most by a factor of 3.1 (and 3.8) slower in dimension 20 (and 5) for targets associated to budgets larger than 10D. Moreover, the (1,4s m)-CMA-ES slightly outperforms the best algorithm on the rotated ellipsoid fu...
Anne Auger, Dimo Brockhoff, Nikolaus Hansen
Added 19 Jul 2010
Updated 19 Jul 2010
Type Conference
Year 2010
Where GECCO
Authors Anne Auger, Dimo Brockhoff, Nikolaus Hansen
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