Traveling Salesman Problem Using Genetic Algorithm

In the field of computer science, the Traveling Salesman Problem (TSP) is a prominent combinatorial optimization challenge aimed at finding a route that minimizes the total cost of visiting a set of cities. Solving this problem can significantly enhance efficiency in domains such as logistics and transportation. One effective approach is utilizing genetic algorithms—a heuristic method inspired by natural selection and genetic principles. Genetic algorithms simulate evolutionary processes by representing solutions as chromosomes and iteratively improving them through genetic operations like selection, crossover, and mutation. In TSP implementations, each chromosome typically encodes a city visitation sequence as an array or permutation. Key functions include fitness evaluation (e.g., calculating total route distance), tournament selection for parent chromosomes, ordered crossover to preserve city sequences, and swap mutation for diversity. This method converges toward near-optimal solutions by maintaining population diversity and exploiting historical search data, making it widely applicable to optimization problems like TSP.