A simple modification in CMA-ES achieving linear time and space complexity

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Abstract

This paper proposes a simple modification of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for high dimensional objective functions, reducing the internal time and space complexity from quadratic to linear. The covariance matrix is constrained to be diagonal and the resulting algorithm, sep-CMA-ES, samples each coordinate independently. Because the model complexity is reduced, the learning rate for the covariance matrix can be increased. Consequently, on essentially separable functions, sep-CMA-ES significantly outperforms CMA-ES . For dimensions larger than a hundred, even on the non-separable Rosenbrock function, the sep-CMA-ES needs fewer function evaluations than CMA-ES .

Original languageEnglish
Title of host publicationParallel Problem Solving from Nature - PPSN X - 10th International Conference, Proceedings
Pages296-305
Number of pages10
DOIs
Publication statusPublished - 26 Nov 2008
Externally publishedYes
Event10th International Conference on Parallel Problem Solving from Nature, PPSN X - Dortmund, Germany
Duration: 13 Sept 200817 Sept 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5199 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Parallel Problem Solving from Nature, PPSN X
Country/TerritoryGermany
CityDortmund
Period13/09/0817/09/08

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