We propose a powerful Reinforced Hybrid Genetic Algorithm (RHGA) for the famous NP-hard Traveling Salesman Problem (TSP). RHGA combines 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 the proposed method.

Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem