The Differential Evolution (DE) algorithm, which is one of the popular optimization algorithms in the category of Evolutionary Algorithms (EAs), is known for its simplicity and wide applicability. Analysing and understanding the working nature of DE algorithm, for its further improvement, is an active research area in Evolutionary Computing (EC) field. In particular studying the role of its control parameters and their effects in its performance needs more attention. As an attempt in this direction, this paper presents evidences to showcase the role of the Scale Factor (F) parameter of DE algorithm through the plots generated based on the studies made from experimental results obtained through a well formulated experimental setup. The experimental set up includes five different benchmarking functions and a crossover-free DE algorithm, in which the crossover component is removed, for capturing better insights about the impact of F. The empirical evidences for the observed inferences are plotted as graphs.
Dhanya M. Dhanalakshmy, Jeyakumar, G., and Velayutham, C., “Crossover-Free Differential Evolution Algorithm to study the impact of Mutation Scale Factor Parameter”, International Journal on Recent Technology and Engineering, vol. 7, pp. 1728-1737, 2019.