Genetic Programming With Niching for Uncertain Capacitated Arc Routing Problem

Shaolin WANG, Yi MEI, Mengjie ZHANG, Xin YAO

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

16 Citations (Scopus)

Abstract

The uncertain capacitated arc routing problem is an important optimization problem with many real-world applications. Genetic programming is considered a promising hyper-heuristic technique to automatically evolve routing policies that can make effective real-time decisions in an uncertain environment. Most existing research on genetic programming hyper-heuristic for the uncertain capacitated arc routing problem only focused on the test performance aspect. As a result, the routing policies evolved by genetic programming are usually too large and complex, and hard to comprehend. To evolve effective, smaller, and simpler routing policies, this article proposes a novel genetic programming approach, which simplifies the routing policies during the evolutionary process using a niching technique. The simplified routing policies are stored in an external archive. We also developed new elitism, parent selection, and breeding schemes for generating offspring from the original population and the archive. The experimental results show that the newly proposed approach can achieve significantly better test performance than the current state-of-the-art genetic programming algorithms for the uncertain capacitated arc routing problem. The evolved routing policies are smaller, and thus potentially more interpretable. © 2022 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
Original languageEnglish
Pages (from-to)73-87
Number of pages15
JournalIEEE Transactions on Evolutionary Computation
Volume26
Issue number1
Early online date7 Jul 2021
DOIs
Publication statusPublished - Feb 2022
Externally publishedYes

Keywords

  • Genetic programming
  • Optimization
  • Routing
  • Sociology
  • Statistics
  • Task analysis
  • Vehicle dynamics

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