Alternative Restart Strategies for CMA-ES

Ilya Loshchilov 1, 2 Marc Schoenauer 1, 2 Michèle Sebag 1, 3
1 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
3 Laboratoire de Recherche en Informatique
LRI - Laboratoire de Recherche en Informatique
Abstract : This paper focuses on the restart strategy of CMA-ES on multi-modal functions. A first alternative strategy proceeds by decreasing the initial step-size of the mutation while doubling the population size at each restart. A second strategy adaptively allocates the computational budget among the restart settings in the BIPOP scheme. Both restart strategies are validated on the BBOB benchmark; their generality is also demonstrated on an independent real-world problem suite related to spacecraft trajectory optimization.
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  • HAL Id : hal-00713415, version 1
  • ARXIV : 1207.0206

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Ilya Loshchilov, Marc Schoenauer, Michèle Sebag. Alternative Restart Strategies for CMA-ES. Parallel Problem Solving From Nature, Sep 2012, Taormina, Italy. pp.296-305. ⟨hal-00713415⟩

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