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. This Symposium aims at bringing researchers and users from academia and industry together to report, interact and review the latest progress in this field, to explore future directions of research and to publicize DE to a wider audience from diverse fields joining the IEEE SSCI 2016 in Athens, Greece and beyond.