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

Ilya Loshchilov 1 Marc Schoenauer 2, 3 Michèle Sebag 4 Nikolaus Hansen 3
1 Laboratory of Intelligent Systems (LIS)
LIS - Laboratory of Intelligent Systems
3 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
4 Laboratoire de Recherche en Informatique
LRI - Laboratoire de Recherche en Informatique
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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Submitted on : Wednesday, June 11, 2014 - 11:47:12 AM
Last modification on : Thursday, July 8, 2021 - 3:49:23 AM
Long-term archiving on: : Thursday, September 11, 2014 - 11:50:27 AM


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  • HAL Id : hal-01003504, version 2
  • ARXIV : 1406.2623



Ilya Loshchilov, Marc Schoenauer, Michèle Sebag, Nikolaus Hansen. Maximum Likelihood-based Online Adaptation of Hyper-parameters in CMA-ES. 13th International Conference on Parallel Problem Solving from Nature (PPSN 2014), Sep 2014, Ljubljana, Slovenia. pp.70-79. ⟨hal-01003504v2⟩



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