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CMA-ES: A Function Value Free Second Order Optimization Method

Nikolaus Hansen 1 
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 give a bird's-eye view introduction to the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and emphasize relevant design aspects of the algorithm, namely its invariance properties. While CMA-ES is gradient and function value free, we show that using the gradient in CMA-ES is possible and can reduce the number of iterations on unimodal, smooth functions.
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Submitted on : Thursday, January 29, 2015 - 8:05:56 PM
Last modification on : Wednesday, October 26, 2022 - 8:09:55 AM
Long-term archiving on: : Wednesday, May 27, 2015 - 1:41:48 PM


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  • HAL Id : hal-01110313, version 1


Nikolaus Hansen. CMA-ES: A Function Value Free Second Order Optimization Method. PGMO COPI 2014, Oct 2014, Paris, France. 2014. ⟨hal-01110313⟩



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