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@inproceedings{rolet:inria00437140,
hal_id = {inria00437140},
url = {http://hal.inria.fr/inria00437140},
title = {{Banditbased Estimation of Distribution Algorithms for Noisy Optimization: Rigorous Runtime Analysis}},
author = {Rolet, Philippe and Teytaud, Olivier},
abstract = {{We show complexity bounds for noisy optimization, in frame works in which noise is stronger than in previously published papers[19]. We also propose an algorithm based on bandits (variants of [16]) that reaches the bound within logarithmic factors. We emphasize the differ ences with empirical derived published algorithms.}},
keywords = {noisy optimization evolutionary algorithms bandits},
language = {Anglais},
affiliation = {Laboratoire de Recherche en Informatique  LRI , TAO  INRIA Futurs , TAO  INRIA Saclay  Ile de France},
booktitle = {{Lion4}},
address = {Venice, Italie},
audience = {internationale },
year = {2010},
pdf = {http://hal.inria.fr/inria00437140/PDF/lion4long.pdf},
}
@inproceedings{coulom:hal00517157,
hal_id = {hal00517157},
url = {http://hal.archivesouvertes.fr/hal00517157},
title = {{Handling Expensive Optimization with Large Noise}},
author = {Coulom, R{\'e}mi and Rolet, Philippe and Sokolovska, Nataliya and Teytaud, Olivier},
abstract = {{This paper exhibits lower and upper bounds on runtimes for expensive noisy optimization problems. Runtimes are expressed in terms of number of fitness evaluations. Fitnesses considered are monotonic transformations of the {\em sphere} function. The analysis focuses on the common case of fitness functions quadratic in the distance to the optimum in the neighborhood of this optimumit is nonetheless also valid for any monotonic polynomial of degree p>2. Upper bounds are derived via a banditbased estimation of distribution algorithm that relies on Bernstein races called REDA. It is known that the algorithm is consistent even in nondifferentiable cases. Here we show that: (i) if the variance of the noise decreases to 0 around the optimum, it can perform optimally for quadratic transformations of the norm to the optimum, (ii) otherwise, it provides a slower convergence rate than the one exhibited empirically by an algorithm called Quadratic Logistic Regression based on surrogate modelsalthough QLR requires a probabilistic prior on the fitness class.}},
keywords = {Noisy optimization, Bernstein races},
language = {Anglais},
affiliation = {SEQUEL  INRIA Lille  Nord Europe , TAO  INRIA Saclay  Ile de France , Laboratoire de Recherche en Informatique  LRI},
booktitle = {{Foundations of Genetic Algorithms (FOGA 2011)}},
pages = {TBA},
address = {Autriche},
editor = {ACM },
audience = {internationale },
year = {2011},
month = Jan,
pdf = {http://hal.archivesouvertes.fr/hal00517157/PDF/foga10noise.pdf},
}
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