A mathematically derived number of resamplings for noisy optimization

Jialin Liu 1, 2 David L. Saint-Pierre 1, 3 Olivier Teytaud 1, 2
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
3 Montefiore institute
LRI - Laboratoire de Recherche en Informatique
Abstract : In Noisy Optimization, one of the most common way to deal with noise is through resampling. In this paper, we compare various resampling rules applied to Evolution Strategy (ES). The goal is to provide a conclusive answer for resampling rules in simple settings. We use a variant of ES as our main algorithm: Self-Adaptive (μ/μ,λ)-Evolution Strategy. We focus our attention on local noisy optimization. In other words, we are interested in situation where reducing the noise is more important than avoiding local minima. We study different sampling rules on the noisy sphere function and compare them experimentally. We conclude that there exists parameter-free formulas that provide adequate resampling rules.
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Jialin Liu, David L. Saint-Pierre, Olivier Teytaud. A mathematically derived number of resamplings for noisy optimization. Companion - Genetic and Evolutionary Computation Conference (GECCO 2014), Jul 2014, Vancouver, Canada. pp.61-62. ⟨hal-00979442⟩

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