Optimizing Genetic Algorithm for Travelling Salesman Problem by Modifying Parameter

The Travelling Salesman Problem (TSP) is a classical optimization algorithm problem in the computer science field. Genetic Algorithm (GA) is an effective technique for solving the TSP. The objective of this research is to find the best possible parameter combination to improve the performance of GA by modifying the parameter values. This study focuses on exploring various combinations of population size, selection method, crossover rate, and mutation rate. The research involves conducting experiments with different parameter combinations to identify the optimal solution for the problem at hand. The findings indicate that a specific set of parameters significantly enhances the performance of the GA, particularly in terms of solution quality and convergence time. To prove that the parameters obtained are effective, standard problems are used to test GA with the tuned and untuned parameters and compare the results. The result shows that GA using the tuned parameters outperformed the untuned parameter. Further research on the exploration of other parameters such as mutation and crossover operators is recommended.