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Conference papers

When does quasi-random work ?

Olivier Teytaud 1, 2
2 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : We experiment the efficiency of quasi-random mutations in evolution strategies in continuous domains from various points of views: (i) non-convexity (ii) convergence rate (iii) non-asymptotic behavior (iv) noise. We conclude that quasi-random mutations are great.
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Submitted on : Friday, June 13, 2008 - 10:42:09 AM
Last modification on : Thursday, July 8, 2021 - 3:48:23 AM
Long-term archiving on: : Friday, September 28, 2012 - 3:52:52 PM


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  • HAL Id : inria-00287863, version 1


Olivier Teytaud. When does quasi-random work ?. Parallel Problem Solving from Nature, Sep 2008, Dortmund, Germany. ⟨inria-00287863⟩



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