Genetic Algorithm for TSP Problem

In computer science, genetic algorithms represent a heuristic approach for solving optimization problems. They are particularly effective for addressing the Traveling Salesman Problem (TSP), which involves finding the shortest possible route that visits each city exactly once and returns to the origin city. Genetic algorithms employ evolutionary principles to search through solution spaces by simulating natural selection and genetic mechanisms. The core methodology maintains a population where each individual represents a potential solution, generating new solutions through genetic operations like crossover and mutation. In TSP implementations, genetic algorithms typically encode routes as chromosomes, where gene sequences represent city visitation orders. Key implementation components include: fitness functions calculating total route distance, selection methods (e.g., tournament or roulette wheel selection), crossover operators (such as ordered or cycle crossover) that combine parent routes, and mutation operators (like swap or inversion mutations) that introduce diversity. Population initialization often uses greedy algorithms or random permutations, while termination criteria may involve generation limits or convergence thresholds. Through iterative evolution, the algorithm progressively optimizes inter-city distances to identify optimal paths, enabling efficient route planning for logistics and combinatorial optimization applications.