Dynamic selection of evolutionary operators based on online learning and fitness landscape analysis

Pietro A. CONSOLI, Yi MEI, Leandro L. MINKU, Xin YAO

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

20 Citations (Scopus)

Abstract

Self-adaptive mechanisms for the identification of the most suitable variation operator in evolutionary algorithms rely almost exclusively on the measurement of the fitness of the offspring, which may not be sufficient to assess the optimality of an operator (e.g., in a landscape with an high degree of neutrality). This paper proposes a novel adaptive operator selection mechanism which uses a set of four fitness landscape analysis techniques and an online learning algorithm, dynamic weighted majority, to provide more detailed information about the search space to better determine the most suitable crossover operator. Experimental analysis on the capacitated arc routing problem has demonstrated that different crossover operators behave differently during the search process, and selecting the proper one adaptively can lead to more promising results. © 2016, The Author(s).
Original languageEnglish
Pages (from-to)3889-3914
Number of pages26
JournalSoft Computing
Volume20
Issue number10
Early online date5 Apr 2016
DOIs
Publication statusPublished - Oct 2016
Externally publishedYes

Bibliographical note

This work was supported by EPSRC (Grant Nos. EP/I010297/1 and EP/J017515/1). Xin Yao was supported by a Royal Society Wolfson Research Merit Award. The authors thank the editors and the anonymous reviewers for their constructive and insightful comments to improve the paper.

Keywords

  • Adaptive operator selection
  • Capacitated arc routing problem
  • Online learning

Fingerprint

Dive into the research topics of 'Dynamic selection of evolutionary operators based on online learning and fitness landscape analysis'. Together they form a unique fingerprint.

Cite this