• DocumentCode
    2217582
  • Title

    Differential evolution for strongly noisy optimization: Use 1.01n resamplings at iteration n and reach the − 1/2 slope

  • Author

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

  • Author_Institution
    CSIE, in National Dong-Hwa University, Hualien, Taiwan
  • fYear
    2015
  • fDate
    25-28 May 2015
  • Firstpage
    338
  • Lastpage
    345
  • 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.
  • Keywords
    Algorithm design and analysis; Error analysis; Noise; Noise measurement; Optimization; Probability; Standards; Differential Evolution; Noisy Optimization; Resampling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation (CEC), 2015 IEEE Congress on
  • Conference_Location
    Sendai, Japan
  • Type

    conf

  • DOI
    10.1109/CEC.2015.7256911
  • Filename
    7256911
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2217582