Increasing the Serial and the Parallel Performance of the CMA-Evolution Strategy with Large Populations

Abstract

The derandomized evolution strategy (ES) with covariance matrix adaptation (CMA), is modified with the goal to speed up the algorithm in terms of needed number of generations. The idea of the modification of the algorithm is to adapt the covariance matrix in a faster way than in the original version by using a larger amount of the information contained in large populations. The original version of the CMA was designed to reliably adapt the covariance matrix in small populations and turned out to be highly efficient in this case. The modification scales up the efficiency to population sizes of up to 10n, where n ist the problem dimension. If enough processors are available, the use of large populations and thus of evaluating a large number of search points per generation is not a problem since the algorithm can be easily parallelized.