Benchmarking a weighted negative covariance matrix update on the BBOB-2010 noiseless testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Mirrored sampling and sequential selection for evolution strategies
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Analyzing the impact of mirrored sampling and sequential selection in elitist evolution strategies
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Mirrored sampling in evolution strategies with weighted recombination
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Bounding the population size of IPOP-CMA-ES on the noiseless BBOB testbed
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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This paper investigates two variants of the well-known Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Active covariance matrix adaptation allows for negative weights in the covariance matrix update rule such that bad steps are (actively) taken into account when updating the covariance matrix of the sample distribution. On the other hand, mirrored mutations via selective mirroring also take the bad steps into account. In this case, they are first evaluated when taken in the opposite direction (mirrored) and then considered for regular selection. In this study, we investigate the difference between the performance of the two variants empirically on the noiseless BBOB testbed. The CMA-ES with selectively mirrored mutations only outperforms the active CMA-ES on the sphere function while the active variant statistically significantly outperforms mirrored mutations on 10 of 24 functions in several dimensions.