Bounding the population size of IPOP-CMA-ES on the noiseless BBOB testbed

Authors:
Tianjun Liao;Thomas Stützle
Affiliations:
IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium;IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium
Venue:
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Year:
2013

Citing 7
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Testing the impact of parameter tuning on a variant of IPOP-CMA-ES with a bounded maximum population size on the noiseless BBOB testbed

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Abstract

A variant of CMA-ES that uses occasional restarts coupled with an increasing population size, which is called IPOP-CMA-ES, has shown to be a top performing algorithm on the BBOB benchmark set. In this paper, we test a mechanism that bounds the maximum size that the population may reach in IPOP-CMA-ES, and we experimentally explore the impact of a maximum population size on the BBOB benchmark set. In the proposed bounding mechanism, we use a maximum population size of 10 × D2 where D is problem dimension. Once the maximum population size is reached or surpassed, the population size is reset to its default starting value λ, which is defined by the λ = 4 + 3 ln(D). Our experimental results show that our scheme for the population size update can lead to improved performances on separable and weakly structured multi-modal functions.