Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Genetic and Evolutionary Computation Conference
AMaLGaM IDEAs in noiseless black-box optimization benchmarking
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 the NEWUOA 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 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
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
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
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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 (1,4ms)-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 (1,4ms)-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 (1,4ms)-CMA-ES slightly outperforms the best algorithm on the rotated ellipsoid function in 20D and would be ranked two on the Gallagher function with 101 peaks in 10D and 20D--being 25 times faster than the BIPOP-CMA-ES and about 3 times faster than the (1+1)-CMA-ES on this function.