IEEE CIS Task Force on Differential Evolution
Ferrante Neri, De Montfort University, UK
Ponnuthurai N. Suganthan, Nanyang Technological University, Singapore
Janez Brest, Institute of Computer Science, FEECS, University of Maribor, Slovenia
Chair History: P. N. Suganthan (2013, 2017), Janez Brest (2014, 2015, 2016)
Why a Tasking Force Group? The main aim of the task force groups of the IEEE CIS Technical Committee on Evolutionary Computation is to promote the research and applications in Differential Evolution.
NEW The 2018 DE special issue in SWEVO journal
Links: IEEE CIS, Evolutionary Computation TC
Differential Evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE is a very simple algorithm, requiring only a few lines of code in most of the existing programming languages. Additionally, it has very few control parameters. Nonetheless, DE exhibits remarkable performance in optimizing a wide variety of optimization problems in terms of final accuracy, convergence speed, and robustness as evidenced by the consistently excellent performance in all of the CEC competitions (http://www3.ntu.edu.sg/home/epnsugan). The last decade has witnessed a rapidly growing research interest in DE.
As demonstrated by the significant increase in the number of research publications on DE in the forms of monographs, edited volumes and archival articles. Although research on and with DE has reached an impressive state, there are still many open problems and new application areas are continually emerging for the algorithm.
The main goal of this task force is to promote research on differential evolution. ...
The scope of this task force includes the following topics: