Application of Genetic Algorithm to Find the Shortest Path Combination Optimization in the Case of the Traveling Salesman Problem

In delivering packages, goods, and in doing a business, location is a critical variable to control. The number of cases is often found in the arrival of late packages because the courier cannot find the Fastest or most efficient route. Determining the most effective distance in a shipment of goods or packages determines customer satisfaction. In this case, the authors make an alternative to search for the shortest path optimization in the Travelling Salesman Problem (TSP) using genetic algorithm methods. With this method, the author wants to analyze and calculate the optimal or shortest route with the data set used. With the principle of a genetic algorithm that resembles the selection of living things with the population as part of each individual, each individual will represent by a fitness value. The application used to make this application is Matlab 2020a. The research results show that the optimal value of generation is 200 generations with the optimal crossover probability of 0.8, and the optimal mutation probability is 0.005. By finding the optimal value of each required variable, the graph of the shortest route will see. This value can be said to be best because the fitness obtained from these results is 0.036 indicating the most optimal value