Publication Type : Conference Proceedings
Publisher : Computational Intelligence in Data Mining (In Smart Innovation, Systems and Technologies)
Source : Computational Intelligence in Data Mining (In Smart Innovation, Systems and Technologies), Smart Innovation, Systems and Technologies, Springer, Volume 32, p.403–416 (2015)
Campus : Coimbatore
School : School of Engineering
Center : Center for Computational Engineering and Networking
Department : Computer Science
Verified : Yes
Year : 2015
Abstract : In this paper we derive an analytical expression to describe the evolution of expected population variance for Differential Evolution (DE) variant—DE/current-to-best/1/bin (as a measure of its explorative power). The derived theoretical evolution of population variance has been validated by comparing it against the empirical evolution of population variance by DE/current-to-best/1/bin on four benchmark functions.
Cite this Research Publication : Dr. Thangavelu S., Dr. Jeyakumar G., Balakrishnan, R. M., and Dr. Shunmuga Velayutham C., “Theoretical Analysis of Expected Population Variance Evolution for a Differential Evolution Variant”, Computational Intelligence in Data Mining (In Smart Innovation, Systems and Technologies), vol. 32. Smart Innovation, Systems and Technologies, Springer, pp. 403–416, 2015.