Projection-Based Restricted Covariance Matrix Adaptation for High Dimension

Youhei Akimoto 1, * Nikolaus Hansen 1
* Corresponding author
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
Abstract : We propose a novel variant of the covariance matrix adaptation evolution strategy (CMA-ES) using a covariance matrix parameterized with a smaller number of parameters. The motivation of a restricted covariance matrix is twofold. First, it requires less internal time and space complexity that is desired when optimizing a function on a high dimensional search space. Second, it requires less function evaluations to adapt the covariance matrix if the restricted covariance matrix is rich enough to express the variable dependencies of the problem. In this paper we derive a computationally efficient way to update the restricted covariance matrix where the model richness of the covariance matrix is controlled by an integer and the internal complexity per function evaluation is linear in this integer times the dimension, compared to quadratic in the dimension in the CMA-ES. We prove that the proposed algorithm is equivalent to the sep-CMA-ES if the covariance matrix is restricted to the diagonal matrix, it is equivalent to the original CMA-ES if the matrix is not restricted. Experimental results reveal the class of efficiently solvable functions depending on the model richness of the covariance matrix and the speedup over the CMA-ES.
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Contributor : Youhei Akimoto <>
Submitted on : Thursday, April 28, 2016 - 1:37:00 AM
Last modification on : Thursday, February 7, 2019 - 4:01:30 PM


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Youhei Akimoto, Nikolaus Hansen. Projection-Based Restricted Covariance Matrix Adaptation for High Dimension. Genetic and Evolutionary Computation Conference 2016, Jul 2016, Denver, United States. ⟨10.1145/2908812.2908863⟩. ⟨hal-01306551v2⟩



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