Bias and variance in continuous EDA

Fabien Teytaud; Olivier Teytaud

inria-00451416, version 1

Bias and variance in continuous EDA

Fabien Teytaud () ^1, 2, 3, Olivier Teytaud () ^2, 3

EA 09 (2009)

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).

1: TAO (INRIA Futurs)
INRIA – CNRS : UMR8623 – Université Paris XI - Paris Sud
2: Laboratoire de Recherche en Informatique (LRI)
CNRS : UMR8623 – Université Paris XI - Paris Sud
3: TAO (INRIA Saclay - Ile de France)
INRIA – CNRS : UMR8623 – Université Paris XI - Paris Sud

inria-00451416, version 1
http://hal.inria.fr/inria-00451416
oai:hal.inria.fr:inria-00451416
From: Fabien Teytaud <>
Submitted on: Friday, 29 January 2010 09:22:26
Updated on: Friday, 29 January 2010 22:48:38

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