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Published on
@inproceedings{teytaud:inria00451416,
hal_id = {inria00451416},
url = {http://hal.inria.fr/inria00451416},
title = {{Bias and variance in continuous EDA}},
author = {Teytaud, Fabien and Teytaud, Olivier},
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 quasirandomization, 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).}},
language = {Anglais},
affiliation = {TAO  INRIA Futurs , Laboratoire de Recherche en Informatique  LRI , TAO  INRIA Saclay  Ile de France},
booktitle = {{EA 09}},
address = {Strasbourg, France},
audience = {internationale },
year = {2009},
month = May,
pdf = {http://hal.inria.fr/inria00451416/PDF/decsigma.pdf},
}
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