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Reports (Research Report) Year : 2008

A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity

Abstract

This report proposes a simple modification of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for high dimensional objective functions that reduces 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 dimension larger than 100, even on the non-separable Rosenbrock function, the sep-CMA-ES needs fewer function evaluations than CMA-ES.
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Dates and versions

inria-00270901 , version 1 (07-04-2008)
inria-00270901 , version 2 (08-04-2008)
inria-00270901 , version 3 (14-04-2008)
inria-00270901 , version 4 (30-06-2008)

Identifiers

  • HAL Id : inria-00270901 , version 4

Cite

Raymond Ros, Nikolaus Hansen. A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity. [Research Report] RR-6498, INRIA. 2008. ⟨inria-00270901v4⟩
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