Differential Evolution for Strongly Noisy Optimization: Use

نویسندگان

  • Shih-Yuan Chiu
  • Ching-Nung Lin
  • Jialin Liu
  • Tsan-Cheng Su
  • Fabien Teytaud
  • Olivier Teytaud
  • Shi-Jim Yen
چکیده

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. Keywords—Noisy Optimization, Differential Evolution, Resampling

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تاریخ انتشار 2015