Maximum likelihood-based online adaptation of hyper-parameters in CMA-ES

Autor: Loshchilov, I., Schoenauer, M., Sebag, M., Nikolaus Hansen

Publikováno v: Scopus-Elsevier
13th International Conference on Parallel Problem Solving from Nature (PPSN 2014)
13th International Conference on Parallel Problem Solving from Nature (PPSN 2014), Sep 2014, Ljubljana, Slovenia. pp.70-79
CIÊNCIAVITAE

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, meanin

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