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Empirical Study of Data-Driven Evolutionary Algorithms in Noisy Environments

Author

Listed:
  • Dalue Lin

    (School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China)

  • Haogan Huang

    (School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China)

  • Xiaoyan Li

    (College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China)

  • Yuejiao Gong

    (School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China)

Abstract

For computationally intensive problems, data-driven evolutionary algorithms (DDEAs) are advantageous for low computational budgets because they build surrogate models based on historical data to approximate the expensive evaluation. Real-world optimization problems are highly susceptible to noisy data, but most of the existing DDEAs are developed and tested on ideal and clean environments; hence, their performance is uncertain in practice. In order to discover how DDEAs are affected by noisy data, this paper empirically studied the performance of DDEAs in different noisy environments. To fulfill the research purpose, we implemented four representative DDEAs and tested them on common benchmark problems with noise simulations in a systematic manner. Specifically, the simulation of noisy environments considered different levels of noise intensity and probability. The experimental analysis revealed the association relationships among noisy environments, benchmark problems and the performance of DDEAs. The analysis showed that noise will generally cause deterioration of the DDEA’s performance in most cases, but the effects could vary with different types of problem landscapes and different designs of DDEAs.

Suggested Citation

  • Dalue Lin & Haogan Huang & Xiaoyan Li & Yuejiao Gong, 2022. "Empirical Study of Data-Driven Evolutionary Algorithms in Noisy Environments," Mathematics, MDPI, vol. 10(6), pages 1-26, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:943-:d:771755
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    References listed on IDEAS

    as
    1. Francesco Marchetti & Edmondo Minisci, 2021. "Genetic Programming Guidance Control System for a Reentry Vehicle under Uncertainties," Mathematics, MDPI, vol. 9(16), pages 1-19, August.
    2. Yuelin Gao & Kaiguang Wang & Chenyang Gao & Yulong Shen & Teng Li, 2019. "Application of Differential Evolution Algorithm Based on Mixed Penalty Function Screening Criterion in Imbalanced Data Integration Classification," Mathematics, MDPI, vol. 7(12), pages 1-36, December.
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