AutoHeuristic-CVRP: Evolving Mutation Operators for Hybrid Genetic Search via Large Language Models

Abstract

This paper presents an approach to automate the evolution of mutation operators in Hybrid Genetic Search (HGS) for the Capacitated Vehicle Routing Problem (CVRP) using Large Language Models (LLMs). Despite the advanced nature of HGS, its mutation heuristics are typically hand-crafted. Recent studies have demonstrated the capacity of LLMs to produce effective routing heuristics; however, these endeavours have predominantly overlooked the utilisation of mutation operators in favour of crossover operators. To bridge this gap, we integrate LLMs within the ruin-and-re create framework of HGS. This work establishes a pathway toward automated fine-grained optimisation of metaheuristic components.