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arXiv · 1406.2623

Maximum Likelihood-based Online Adaptation of Hyper-parameters in CMA-ES

Ilya Loshchilov·Marc Schoenauer·Michèle Sebag·Nikolaus Hansen
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Abstract

The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is widely accepted as a robust derivative-free continuous optimization algorithm for non-linear and non-convex optimization problems. CMA-ES is well known to be almost parameterless, meaning that only one hyper-parameter, the population size, is proposed to be tuned by the user. In this paper, we propose a principled approach called self-CMA-ES to achieve the online adaptation of CMA-ES hyper-parameters in order to improve its overall performance. Experimental results show that for larger-than-default population size, the default settings of hyper-parameters of CMA-ES are far from being optimal, and that self-CMA-ES allows for dynamically approaching optimal settings.

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Ilya Loshchilov, Marc Schoenauer, Michèle Sebag, Nikolaus Hansen. 2014-06-10. Maximum Likelihood-based Online Adaptation of Hyper-parameters in CMA-ES. https://arxiv.org/abs/1406.2623

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