Maximum Likelihood-Based Online Adaptation of Hyper-Parameters in CMA-ES
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- Loshchilov I., Schoenauer M., Sebag M., Hansen N. (2014) Maximum Likelihood-Based Online Adaptation of Hyper-Parameters in CMA-ES. In: Bartz-Beielstein T., Branke J., Filipič B., Smith J. (eds) Parallel Problem Solving from Nature – PPSN XIII. PPSN 2014. Lecture Notes in Computer Science, vol 8672. Springer, Cham
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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