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.
Original language | English |
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Pages (from-to) | 73-87 |
Number of pages | 15 |
Journal | IEEE Transactions on Evolutionary Computation |
Volume | 26 |
Issue number | 1 |
Early online date | 7 Jul 2021 |
DOIs | |
Publication status | Published - Feb 2022 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2022 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
Funding
This work was supported in part by the Marsden Fund of New Zealand Government under Contract VUW1509 and Contract VUW1614; in part by the Research Institute of Trustworthy Autonomous Systems; in part by the Guangdong Provincial Key Laboratory under Grant 2020B121201001; in part by the Program for Guangdong Introducing Innovative and Enterpreneurial Teams under Grant 2017ZT07X386; in part by the Shenzhen Science and Technology Program under Grant KQTD2016112514355531; and in part by the Program for University Key Laboratory of Guangdong Province under Grant 2017KSYS008. The work of Shaolin Wang was supported by the Victoria University of Wellington Ph.D. Scholarship.
Keywords
- Genetic programming
- Optimization
- Routing
- Sociology
- Statistics
- Task analysis
- Vehicle dynamics