Algorithm Portfolios for Noisy Optimization

Marie-Liesse Cauwet 1 Jialin Liu 1 Rozière Baptiste 1 Olivier Teytaud 1
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 solvers is a set of solvers equipped with an algorithm selection tool for distributing the computational power among them. Portfolios are widely and successfully used in combinatorial optimization. In this work, we study portfolios of noisy optimization solvers. We obtain mathematically proved performance (in the sense that the portfolio performs nearly as well as the best of its solvers) by an ad hoc portfolio algorithm dedicated to noisy optimization. A somehow surprising result is that it is better to compare solvers with some lag, i.e., propose the current recommendation of best solver based on their performance earlier in the run. An additional finding is a principled method for distributing the computational power among solvers in the portfolio.
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Article dans une revue
Annals of Mathematics and Artificial Intelligence, Springer Verlag, 2015, pp.1-30. 〈10.1007/s10472-015-9486-2〉
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Marie-Liesse Cauwet, Jialin Liu, Rozière Baptiste, Olivier Teytaud. Algorithm Portfolios for Noisy Optimization. Annals of Mathematics and Artificial Intelligence, Springer Verlag, 2015, pp.1-30. 〈10.1007/s10472-015-9486-2〉. 〈hal-01223113〉

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