Benchmarking the local metamodel CMA-ES on the noiseless BBOB'2013 test bed
Anne Auger, Dimo Brockhoff, Nikolaus Hansen · 2013
This paper evaluates the performance of a variant of the local-meta-model CMA-ES (lmm-CMA) in the BBOB 2013 expensive setting. The lmm-CMA is a surrogate variant of the CMA-ES algorithm. Function evaluations are saved by building, with weighted regression, full quadratic meta-models to estimate the candidate solutions' function values. The quality of the approximation is appraised by checking how much the predicted rank changes when evaluating a fraction of the candidate solutions on the original objective function. The results are compared with the CMA-ES without meta-modeling and with previously benchmarked algorithms, namely BFGS, NEWUOA and saACM.