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Online Model Selection for Restricted Covariance Matrix Adaptation

Youhei Akimoto 1 Nikolaus Hansen 1
1 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
Abstract : We focus on a variant of covariance matrix adaptation evolution strategy (CMA-ES) with a restricted covariance matrix model, namely VkD-CMA, which is aimed at reducing the internal time complexity and the adaptation time in terms of function evaluations. We tackle the shortage of the VkD-CMA—the model of the restricted covariance matrices needs to be selected beforehand. We propose a novel mechanism to adapt the model online in the VkD-CMA. It eliminates the need for advance model selection and leads to a performance competitive with or even better than the algorithm with a nearly optimal but fixed model.
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Contributor : Youhei Akimoto <>
Submitted on : Monday, June 20, 2016 - 12:34:29 AM
Last modification on : Wednesday, October 14, 2020 - 4:00:15 AM


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  • HAL Id : hal-01333840, version 1


Youhei Akimoto, Nikolaus Hansen. Online Model Selection for Restricted Covariance Matrix Adaptation. Parallel Problem Solving from Nature – PPSN XIV, Sep 2016, Edinburgh, United Kingdom. pp.3-13. ⟨hal-01333840⟩



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