Online Model Selection for Restricted Covariance Matrix Adaptation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9921)


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.





This work is partially supported by JSPS KAKENHI Grant Number 15K16063.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Faculty of EngineeringShinshu UniversityNaganoJapan
  2. 2.Inria, Research Centre Saclay – Île-de-FrancePalaiseauFrance

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