Differential Evolution for Strongly Noisy Optimization: Use 1.01$^n$ Resamplings at Iteration n and Reach the -1/2 Slope - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2015

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

Résumé

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.
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Dates et versions

hal-01120892 , version 1 (26-02-2015)

Identifiants

  • HAL Id : hal-01120892 , version 1

Citer

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. ⟨hal-01120892⟩
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