Solving TSP Using Genetic Algorithm in MATLAB

Using genetic algorithms in MATLAB to solve the Traveling Salesman Problem (TSP) represents a classic optimization approach. Genetic algorithms mimic biological evolution processes by simulating natural selection, crossover, and mutation operations to search for optimal solutions. When solving TSP, the algorithm encodes each possible route as a chromosome population, then iteratively improves solutions through fitness evaluation, selection, crossover, and mutation operations. Key implementation aspects include: - Route encoding using permutation representation where cities are arranged in visit order - Fitness function calculation based on total route distance minimization - Selection methods like tournament or roulette wheel selection - Crossover operators (e.g., ordered crossover) to combine parent solutions - Mutation operations (e.g., swap mutation) to maintain population diversity - Termination criteria based on maximum generations or convergence thresholds This method proves highly effective for TSP by handling large-scale problems and producing near-optimal solutions. The MATLAB implementation typically involves creating population initialization functions, designing genetic operators, and implementing convergence monitoring. The ga() function from Global Optimization Toolbox can also be utilized with custom fitness functions and constraints. Therefore, combining MATLAB's computational capabilities with genetic algorithms provides a robust framework worth exploring for TSP solutions.