Covariance Matrix Adaptation Revisited --- The CMSA Evolution Strategy ---

Authors:
Hans-Georg Beyer;Bernhard Sendhoff
Affiliations:
Vorarlberg University of Applied Sciences, Dornbirn, Austria A-6850;Honda Research Institute Europe GmbH, Offenbach/Main, Germany D-63073
Venue:
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Year:
2008

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Abstract

The covariance matrix adaptation evolution strategy (CMA-ES) rates among the most successful evolutionary algorithms for continuous parameter optimization. Nevertheless, it is plagued with some drawbacks like the complexity of the adaptation process and the reliance on a number of sophisticatedly constructed strategy parameter formulae for which no or little theoretical substantiation is available. Furthermore, the CMA-ES does not work well for large population sizes. In this paper, we propose an alternative --- simpler --- adaptation step of the covariance matrix which is closer to the "traditional" mutative self-adaptation. We compare the newly proposed algorithm, which we term the CMSA-ES, with the CMA-ES on a number of different test functions and are able to demonstrate its superiority in particular for large population sizes.