Maximum Likelihood-Based Online Adaptation of Hyper-Parameters in CMA-ES

The Covariance Matrix Adaptation Evolution Strategy (CMAES) 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 selfCMA-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 hyperparameters of CMA-ES are far from being optimal, and that self-CMAES allows for dynamically approaching optimal settings.