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

Black-box optimization benchmarking of IPOP-saACM-ES and BIPOP-saACM-ES on the BBOB-2012 noiseless testbed