The Implementation of Genetic Algorithm in Path Optimization

Traveling Salesman Problem (TSP) is a classical problem in Artificial Intelligence (AI) field. Since 1800s when first mathematical problems related to TSP was treated, it became an interesting topic of optimization problem to be studied. In this project, TSP will be used to model and easy visualize the path optimization problem and Genetic Algorithm (GA) was chosen to be implemented in resolving the problem. This project will focus on the static variable referring to the length of distance as the fitness function of optimization. The idea of resolving TSP study is to come out with the shortest path among all possible solutions of tour to be taken. However, the major concern here is how to ensure that the optimum result is obtained. Therefore, the operators and parameters of GA itself were studied in depth particularly the mutation operator. Experiments were conducted to measure the effectiveness of two different types of mutation method namely swapping method and inversion method. The comparison of both performances in achieving optimum result had been analyzed in detail. Therefore, the implementation ofGA in path optimization can be ascertained offering a compelling result