Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Genetic and Evolutionary Computation Conference
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Benchmarking a BI-population CMA-ES on the BBOB-2009 noisy testbed
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Benchmarking sep-CMA-ES on the BBOB-2009 noisy testbed
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Benchmarking the (1+1)-CMA-ES on the BBOB-2009 function testbed
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Benchmarking the (1+1)-CMA-ES on the BBOB-2009 noisy testbed
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Mirrored variants of the (1,2)-CMA-ES compared on the noiseless BBOB-2010 testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Mirrored variants of the (1,4)-CMA-ES compared on the noiseless BBOB-2010 testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Mirrored variants of the (1,2)-CMA-ES compared on the noisy BBOB-2010 testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Mirrored variants of the (1,4)-CMA-ES compared on the noisy BBOB-2010 testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Investigating the impact of sequential selection in the (1,2)-CMA-ES on the noisy BBOB-2010 testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Investigating the impact of sequential selection in the (1,4)-CMA-ES on the noisy BBOB-2010 testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Analyzing the impact of mirrored sampling and sequential selection in elitist evolution strategies
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Direct Model-Based Tracking of 3D Object Deformations in Depth and Color Video
International Journal of Computer Vision
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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 (1,4ms)-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 (1,4ms)-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 (1,4ms)-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.