Benchmarking the (1,4)-CMA-ES With Mirrored Sampling and Sequential Selection on the Noiseless BBOB-2010 Testbed [Black-Box Optimization Benchmarking Workshop]

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 R. Recently, mirrored samples and sequential selection have been introduced within CMA-ES to improve its local search performances. In this paper, we benchmark the (1,4m)-CMA-ES which implements mirrored samples and sequential selection on the BBOB-2010 noiseless testbed. Independent restarts are conducted until a maximal number of 10D 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 (1,4m)-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 (1,4m)-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-CMAES and about 3 times faster than the (1+1)-CMA-ES on this function.