Solving Traveling Salesman Problem Using Genetic Algorithm

Using genetic algorithms to solve the Traveling Salesman Problem (TSP) represents a widely adopted computational approach. This method demonstrates scalability across problem sizes and proves particularly efficient for the five-city TSP scenario presented here. The implementation features key genetic algorithm components including population initialization with random city permutations, fitness evaluation based on total path distance, tournament selection for parent chromosomes, ordered crossover (OX) for offspring generation, and swap mutation operators to maintain genetic diversity. The algorithm iteratively evolves solutions through generations, utilizing elitism to preserve best-performing routes while progressively minimizing total travel distance. Below we provide complete source code with annotations, demonstrating how the algorithm constructs optimal paths ensuring each city is visited exactly once. The solution's flexibility and adaptability make genetic algorithms a powerful tool for combinatorial optimization problems like TSP, balancing exploration of solution space with convergence toward optimal routes.