Publication Type:

Book Chapter

Source:

Computational Intelligence in Data Mining (In Smart Innovation, Systems and Technologies), Springer, Volume 32, p.403–416 (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

S. Thangavelu, Dr. Jeyakumar G., Balakrishnan, R. M., and Dr. Shunmuga Velayutham C., “Theoretical Analysis of Expected Population Variance Evolution for a Differential Evolution Variant”, in Computational Intelligence in Data Mining (In Smart Innovation, Systems and Technologies), vol. 32, Springer, 2015, pp. 403–416.

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