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Algorithm Portfolios for Noisy Optimization: Compare Solvers Early

Marie-Liesse Cauwet 1, 2 Jialin Liu 1, 2 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
Abstract : Noisy optimization is the optimization of objective functions corrupted by noise. A portfolio of algorithms is a set of algorithms equipped with an algorithm selection tool for distributing the compu- tational power among them. We study portfolios of noisy optimization solvers, show that different settings lead to dramatically different perfor- mances, obtain mathematically proved adaptivity by an ad hoc selection algorithm dedicated to noisy optimization. A somehow surprising result is that it is better to compare solvers with some lag; i.e., recommend the current recommendation of the best solver, selected from a comparison based on their recommendations earlier in the run.
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https://hal.inria.fr/hal-00926638
Contributor : Jialin Liu <>
Submitted on : Friday, April 4, 2014 - 10:38:32 AM
Last modification on : Thursday, June 17, 2021 - 3:47:06 AM
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Marie-Liesse Cauwet, Jialin Liu, Olivier Teytaud. Algorithm Portfolios for Noisy Optimization: Compare Solvers Early. Learning and Intelligent Optimization Conference, Feb 2014, Florida, United States. ⟨hal-00926638⟩

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