Machine Learning for Evolutionary Computation - the Vehicle Routing Problems Competition

Meng, Weiyao; Qu, Rong; Pillay, Nelishia

Abstract

The Competition of Machine Learning for Evolutionary Computation for Solving Vehicle Routing Problems (ML4VRP) seeks to bring together machine learning and evolutionary computation communities to propose innovative techniques for vehicle routing problems (VRPs), aiming to advance machine learning-assisted evolutionary computation that works well across different instances of the VRPs. This paper overviews the key information of the competition.

Citation

Meng, W., Qu, R., & Pillay, N. (2023, July). Machine Learning for Evolutionary Computation - the Vehicle Routing Problems Competition. Presented at GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Melbourne VIC Australia

• Presentation Conference Type: Edited Proceedings
• Conference Name: GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion
• Start Date: Jul 14, 2023
• End Date: Jul 18, 2024
• Acceptance Date: Jul 14, 2024
• Online Publication Date: Aug 1, 2024
• Publication Date: Jul 14, 2024
• Deposit Date: Jan 6, 2025
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Pages: 13-14
• Book Title: GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion
• DOI: https://doi.org/10.1145/3638530.3664046
• Keywords: Machine learning, Evolutionary computation, Meta-heuristics, Vehicle routing
• Public URL: https://nottingham-repository.worktribe.com/output/37949899
• Publisher URL: https://dl.acm.org/doi/10.1145/3638530.3664046
• Additional Information: Published: 2024-08-01