Curriculum Learning in Genetic Programming Guided Local Search for Large-scale Vehicle Routing Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.8620

@InProceedings{DBLP:conf/cec/Liu0025,

• author = "Saining Liu and Yi Mei and Mengjie Zhang",
• title = "Curriculum Learning in Genetic Programming Guided Local Search for Large-scale Vehicle Routing Problems",
• booktitle = "2025 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2025",
• editor = "Yaochu Jin and Thomas Baeck",
• address = "Hangzhou, China",
• month = "8-12 " # jun,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Training, Adaptation models, Vehicle routing, Evolutionary computation, Search problems, Complexity theory, Convergence, curriculum learning, genetic programming guided local search, large-scale vehicle routing problem, utility function",
• isbn13 = "979-8-3315-3432-5",
• timestamp = "Mon, 30 Jun 2025 01:00:00 +0200",
• biburl = "https://dblp.org/rec/conf/cec/Liu0025.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• URL = "https://doi.org/10.1109/CEC65147.2025.11042951",
• DOI = "10.1109/CEC65147.2025.11042951",
• abstract = "Manually designing (meta-)heuristics for the Vehicle Routing Problem (VRP) is a challenging task that requires significant domain expertise. Recently, data-driven approaches have emerged as a promising solution, automatically learning heuristics that perform well on training instances and generalise to unseen test cases. Such an approach learns (meta-)heuristics that can perform well on the training instances, expecting it to generalise well on the unseen test instances. A recent method, named GPGLS, uses Genetic Programming (GP) to learn the utility function in Guided Local Search (GLS) and solved large scale VRP effectively. However, the selection of appropriate training instances during the learning process remains an open question, with most existing studies including GPGLS relying on random instance selection. To address this, we propose a novel method, CL-GPGLS, which integrates Curriculum Learning (CL) into GPGLS. Our approach leverages a predefined curriculum to introduce training instances progressively, starting with simpler tasks and gradually increasing complexity, enabling the model to better adapt and optimise for large-scale VRP (LSVRP). Extensive experiments verify the effectiveness of CL-GPGLS, demonstrating significant performance improvements over three baseline methods.",
• notes = "also known as \cite{liu:2025:CEC2} \cite{11042951}",

}

Genetic Programming entries for Saining Liu Yi Mei Mengjie Zhang

Citations