Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem

We propose a novel method called the Reinforced Hybrid Genetic Algorithm (RHGA) for solving the famous NP-hard Traveling Salesman Problem (TSP). Specifically, we combine a reinforcement learning technique with the well-known Edge Assembly Crossover genetic algorithm (EAX-GA) and the Lin-Kernighan-Helsgaun (LKH) local search heuristic. With the help of the proposed hybrid mechanism, the genetic evolution of EAX-GA and the local search of LKH can boost each other's performance. And the reinforcement learning technique based on Q-learning further promotes the hybrid genetic algorithm. Experimental results on 138 well-known and widely used TSP benchmarks with the number of cities ranging from 1,000 to 85,900 demonstrate the excellent performance of RHGA, that outperforms EAX-GA and LKH significantly.