Solving TSP Problem Using Genetic Algorithm

The following MATLAB source code provides a genetic algorithm implementation for solving the Traveling Salesman Problem (TSP). You can modify and optimize this code according to your specific requirements. First, we need to define genetic algorithm parameters including population size, crossover rate, and mutation rate. Population size determines the number of individuals in each generation for evolutionary computation. Crossover rate specifies the probability of genetic information exchange between parent chromosomes during reproduction. Mutation rate controls the likelihood of random gene alterations to maintain population diversity. Next, we initialize the population using random permutation generation. The implementation creates random tours representing potential solutions to the TSP. The algorithm then iteratively applies selection, crossover, and mutation operations across generations until reaching maximum iterations or finding an optimal solution. Selection typically uses tournament or roulette wheel methods to choose parents based on fitness (tour length). Crossover operations like ordered crossover (OX) or partially mapped crossover (PMX) combine parent routes while preserving valid tour structures. Mutation operators such as swap or inversion introduce small changes to prevent premature convergence. Finally, the algorithm outputs the optimal solution with the shortest path distance and corresponding route sequence. The code structure allows easy modification of objective functions, operator implementations, and termination criteria to enhance performance for different TSP instances. Key MATLAB functions involved include population initialization using randperm, fitness calculation with distance matrices, and custom implementations for genetic operators.