Source Code Program for Solving TSP Using Genetic Algorithm

This source code program implements a Genetic Algorithm (GA) solution for the Traveling Salesman Problem (TSP), providing precise optimization for 50-city instances. The genetic algorithm mimics natural evolutionary processes through chromosome encoding, selection, crossover, and mutation operations to solve combinatorial optimization problems. For TSP implementation, the algorithm typically represents city tours as permutations using path encoding, with fitness evaluation based on total route distance. Key components include tournament selection for parent choosing, ordered crossover (OX) for offspring generation, and swap mutation for maintaining population diversity. The iterative optimization process continuously evolves populations through generations, converging toward optimal or near-optimal routes where the salesman visits all cities exactly once and returns to the starting point. The source code provides an effective framework for handling large-scale TSP instances, featuring modular design with separate functions for initialization, fitness calculation, genetic operators, and convergence monitoring.