Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
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
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
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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Derandomization by means of mirrored samples has been recently introduced to enhance the performances of (1,λ)-Evolution-Strategies (ESs) with the aim of designing fast and robust stochastic local search algorithms. This paper compares on the BBOB-2010 noiseless benchmark testbed two variants of the (1,4)-CMA-ES where the mirrored samples are used. Independent restarts are conducted up to a total budget of 104 D function evaluations, where D is the dimension of the search space. The results show that the improved variants are significantly faster than the baseline (1,4)-CMA-ES on 4 functions in 20D (respectively 7 when using sequential selection in addition) by a factor of up to 3 (on the attractive sector function). In no case, the (1,4)-CMA-ES is significantly faster on any tested target function value in 5D and 20D. Moreover, the algorithm employing both mirroring and sequential selection is significantly better than the algorithm without sequentialism on five functions in 20D with expected running times that are about 20% smaller.