Why one must use reweighting in Estimation Of Distribution Algorithms

Fabien Teytaud 1, 2 Olivier Teytaud 3, 4, 5
1 INRIA Futurs
2 UP11 UFR Sciences - Université Paris-Sud 11 - Faculté des Sciences
3 TANC - Algorithmic number theory for cryptology
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], Inria Saclay - Ile de France
4 LRI - Laboratoire de Recherche en Informatique
5 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 study the update of the distribution in Estimation of Distribution Algorithms, and show that a simple modification leads to unbiased estimates of the optimum. The simple modification (based on a proper reweighting of estimates) leads to a strongly improved behavior in front of premature convergence.
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https://hal.inria.fr/inria-00369780
Contributor : Olivier Teytaud <>
Submitted on : Saturday, March 21, 2009 - 8:51:33 AM
Last modification on : Wednesday, March 27, 2019 - 4:41:29 PM
Long-term archiving on : Thursday, June 10, 2010 - 5:57:17 PM

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Fabien Teytaud, Olivier Teytaud. Why one must use reweighting in Estimation Of Distribution Algorithms. GECCO, 2009, Montréal, Canada. ⟨inria-00369780⟩

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