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Bias and variance in continuous EDA

Fabien Teytaud 1, 2, 3 Olivier Teytaud 2, 3 
1 TANC - Algorithmic number theory for cryptology
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], Inria Saclay - Ile de France
3 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 : Estimation of Distribution Algorithms are based on statistical estimates. We show that when combining classical tools from statistics, namely bias/variance decomposition, reweighting and quasi-randomization, we can strongly improve the convergence rate. All modifications are easy, compliant with most algorithms, and experimentally very efficient in particular in the parallel case (large offsprings).
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Submitted on : Friday, January 29, 2010 - 9:22:26 AM
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Fabien Teytaud, Olivier Teytaud. Bias and variance in continuous EDA. EA 09, Oct 2009, Strasbourg, France. ⟨inria-00451416⟩

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