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

@inproceedings{Loshchilov2014MaximumLO,
  title={Maximum Likelihood-based Online Adaptation of Hyper-parameters in CMA-ES},
  author={Ilya Loshchilov and Marc Schoenauer and Mich{\`e}le Sebag and Nikolaus Hansen},
  booktitle={PPSN},
  year={2014}
}
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… CONTINUE READING
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