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Sieves method in fuzzy control: logarithmically increase the number of rules

Vincent Berthier 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 : The Sieves method, in statistics, consists in extending a model progressively, as new data are made available. Typically, parameters are progressively added in a statistical estimation method while new samples are provided. We propose an adaptation of the Sieves method in optimization. Decision variables are progressively added while new fitness evaluations are received. We experiment the method on a simple set of noisy optimization problems, and then on a fuzzy control problem applied to unit commitment. The obtained algorithm is simple, applicable to various optimization algorithms (not only evolutionary optimization), and seemingly robust.
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Submitted on : Thursday, October 15, 2015 - 7:23:46 AM
Last modification on : Thursday, July 8, 2021 - 3:49:46 AM
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  • HAL Id : hal-01215806, version 1


Vincent Berthier, Olivier Teytaud. Sieves method in fuzzy control: logarithmically increase the number of rules. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Aug 2015, Istanbul, Turkey. pp.1 - 9. ⟨hal-01215806⟩



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