Population Initialization: The first step is to create an initial population, which consists of a set of randomly generated routes, each representing a possible path that the traveling salesman can take.
Fitness Evaluation: The fitness of each route is calculated, typically as the total distance of the path. The objective is to minimize this distance.
Selection: In each iteration of the genetic algorithm, a selection process is performed to choose individuals from the population for the next generation. Selection methods like roulette wheel selection or tournament selection are commonly used.
Crossover: Selected individuals are paired, and a crossover operation is applied. This involves combining the routes of two parents to create a new child route.
Mutation: Some individuals undergo mutation to introduce diversity into the population. Mutation can involve random changes in the route, such as swapping the positions of two cities.
New Population Generation: After selection, crossover, and mutation, a new population is formed.
Termination Condition: The algorithm continues to iterate through these steps until a termination condition is met, such as a specified number of generations or a time limit.
Identifying the Best Solution: Once the algorithm terminates, the route with the best fitness (shortest distance) is considered the optimal solution to the TSP.