Increasing the Serial and the Parallel Performance of the
CMA-Evolution Strategy with Large Populations
Mueller, S.D., N. Hansen, P. Koumoutsakos
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 is the problem dimension. If enough processors are available,
the use of large populations and thus of evaluating a large number of
search point per generation is not a problem since the algorithm can be
easily prallelized.
In: Seventh International Conference on Parallel Problem Solving from
Nature PPSN VII, Proceedings, pp. 422-431, Berlin: Springer.
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