Benchmarking the (1,4)-CMA-ES with mirrored sampling and sequential selection on the noiseless BBOB-2010 testbed
Anne Auger, Dimo Brockhoff, Nikolaus Hansen · 2010
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.