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

  • Ilya Loshchilov
  • Marc Schoenauer
  • Michèle Sebag
  • Nikolaus Hansen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8672)


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.


Evolutionary Computation Covariance Matrix Adaptation Evolution Strategy Rosenbrock Function Evolve Fuzzy System Auxiliary Optimization Problem 
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ilya Loshchilov
    • 1
  • Marc Schoenauer
    • 2
    • 3
  • Michèle Sebag
    • 3
    • 2
  • Nikolaus Hansen
    • 2
    • 3
  1. 1.Laboratory of Intelligent SystemsÉcole Polytechnique Fédérale de LausanneSwitzerland
  2. 2.TAO Project-teamINRIA SaclayÎle-de-FranceFrance
  3. 3.Laboratoire de Recherche en Informatique (UMR CNRS 8623)Université Paris-SudOrsay CedexFrance

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