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

TL;DR

This paper introduces CL-GPGLS, a novel curriculum learning approach integrated with genetic programming-guided local search, significantly improving large-scale vehicle routing problem solutions by progressively training on increasingly complex instances.

Contribution

It proposes a new curriculum learning method for GPGLS, enhancing training instance selection and performance on large-scale VRPs.

Findings

CL-GPGLS outperforms baseline methods in large-scale VRP solutions.

Progressive training with curriculum learning improves model adaptation.

Significant performance gains demonstrated through extensive experiments.

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 generalize to unseen test cases. Such an approach learns (meta-)heuristics that can perform well on the training instances, expecting it to generalize 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 optimize for large-scale VRP (LSVRP). Extensive experiments verify the effectiveness of CL-GPGLS, demonstrating significant performance improvements over three baseline methods.