Differential Evolution for Strongly Noisy Optimization: Use 1.01$^n$ Resamplings at Iteration n and Reach the -1/2 Slope

Shih-Yuan Chiu 1 Ching-Nung Lin 1 Jialin Liu 2, 3 Tsang-Cheng Su 1 Fabien Teytaud 4 Olivier Teytaud 2, 3 Shi-Jim Yen 1
2 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 : This paper is devoted to noisy optimization in case of a noise with standard deviation as large as variations of the fitness values, specifically when the variance does not decrease to zero around the optimum. We focus on comparing methods for choosing the number of resamplings. Experiments are performed on the differential evolution algorithm. By mathematical analysis, we design a new rule for choosing the number of resamplings for noisy optimization, as a function of the dimension, and validate its efficiency compared to existing heuristics.
Type de document :
Communication dans un congrès
2015 IEEE Congress on Evolutionary Computation (IEEE CEC), May 2015, Sendai, Japan. 2015, 〈http://www.cec2015.org〉
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Soumis le : jeudi 26 février 2015 - 17:56:11
Dernière modification le : jeudi 11 janvier 2018 - 06:22:14
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Shih-Yuan Chiu, Ching-Nung Lin, Jialin Liu, Tsang-Cheng Su, Fabien Teytaud, et al.. Differential Evolution for Strongly Noisy Optimization: Use 1.01$^n$ Resamplings at Iteration n and Reach the -1/2 Slope. 2015 IEEE Congress on Evolutionary Computation (IEEE CEC), May 2015, Sendai, Japan. 2015, 〈http://www.cec2015.org〉. 〈hal-01120892〉

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