Online algorithm configuration for differential evolution algorithm

Changwu HUANG, Hao BAI, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

6 Citations (Scopus)

Abstract

The performance of evolutionary algorithms (EAs) is strongly affected by their configurations. Thus, algorithm configuration (AC) problem, that is, to properly set algorithm’s configuration, including the operators and parameter values for maximizing the algorithm’s performance on given problem(s) is an essential and challenging task in the design and application of EAs. In this paper, an online algorithm configuration (OAC) approach is proposed for differential evolution (DE) algorithm to adapt its configuration in a data-driven way. In our proposed OAC, the multi-armed bandit algorithm is adopted to select trial vector generation strategies for DE, and the kernel density estimation method is used to adapt the associated control parameters during the evolutionary search process. The performance of DE algorithm using the proposed OAC (OAC-DE) is evaluated on a benchmark set of 30 bound-constrained numerical optimization problems and compared with several adaptive DE variants. Besides, the influence of OAC’s hyper-parameter on its performance is analyzed. The comparison results show OAC-DE achieves better average performance than the compared algorithms, which validates the effectiveness of the proposed OAC. The sensitivity analysis indicates that the hyper-parameter of OAC has little impact on OAC-DE’s performance. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Original languageEnglish
Pages (from-to)9193-9211
Number of pages19
JournalApplied Intelligence
Volume52
Issue number8
Early online date3 Jan 2022
DOIs
Publication statusPublished - Jun 2022
Externally publishedYes

Bibliographical note

This work was supported by the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2019A1515110575), the Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (Grant No. 2017ZT07X386), the Shenzhen Science and Technology Program (Grant No. KQTD2016112514355531), Shenzhen Basic Research Program (No. JCYJ20180504165652917), and the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008).

Keywords

  • Adaptive parameter control
  • Automatic algorithm configuration
  • Differential evolution algorithm
  • Kernel density estimation
  • Machine learning
  • Multi-armed bandit

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