TY - JOUR AU - Nababan, Erna Budhiarti AU - Sitompul, Opim Salim AU - Cancer, Yuni PY - 2018/08/03 Y2 - 2024/03/29 TI - Genetic Algorithms Dynamic Population Size with Cloning in Solving Traveling Salesman Problem JF - Data Science: Journal of Computing and Applied Informatics JA - Data Science: J. of Computing and Appl. Informatics VL - 2 IS - 2 SE - DO - 10.32734/jocai.v2.i2-326 UR - https://talenta.usu.ac.id/JoCAI/article/view/326 SP - 87-100 AB - <p>Population size of classical genetic algorithm is determined constantly. Its size remains constant over the run. For more complex problems, larger population sizes need to be avoided from early convergence to produce local optimum. Objective of this research is to evaluate population resizing i.e. dynamic population sizing for Genetic Algorithm (GA) using cloning strategy. We compare performance of proposed method and traditional GA employed to Travelling Salesman Problem (TSP) of A280.tsp taken from TSPLIB. Result shown that GA with dynamic population size exceed computational time of traditional GA.</p> ER -