New mechanism of combination crossover operators in genetic algorithm for solving the traveling salesman problem
This work addresses the traveling salesman problem for optimization researchers, but it is incremental as it builds on existing genetic algorithm methods.
The authors tackled the traveling salesman problem by proposing two new crossover operators and a combination mechanism in genetic algorithms, achieving better min and mean cost values compared to a baseline GA using MSCX on TSP-Lib instances.
Traveling salesman problem (TSP) is a well-known in computing field. There are many researches to improve the genetic algorithm for solving TSP. In this paper, we propose two new crossover operators and new mechanism of combination crossover operators in genetic algorithm for solving TSP. We experimented on TSP instances from TSP-Lib and compared the results of proposed algorithm with genetic algorithm (GA), which used MSCX. Experimental results show that, our proposed algorithm is better than the GA using MSCX on the min, mean cost values.