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
