  In this paper, we study the performance of IPOP-saACM-ES and BIPOP-saACM-ES,
recently proposed self-adaptive surrogate-assisted Covariance Matrix Adaptation
Evolution Strategies. Both algorithms were tested using restarts till a total
number of function evaluations of $10^6D$ was reached, where $D$ is the
dimension of the function search space. We compared surrogate-assisted
algorithms with their surrogate-less versions IPOP-saACM-ES and BIPOP-saACM-ES,
two algorithms with one of the best overall performance observed during the
BBOB-2009 and BBOB-2010. The comparison shows that the surrogate-assisted
versions outperform the original CMA-ES algorithms by a factor from 2 to 4 on 8
out of 24 noiseless benchmark problems, showing the best results among all
algorithms of the BBOB-2009 and BBOB-2010 on Ellipsoid, Discus, Bent Cigar,
Sharp Ridge and Sum of different powers functions.
