Special Session on:
Differential Evolution: Past, Present and Future
Aim and Scope
Differential evolution (DE) emerged as a simple and powerful stochastic real-parameter optimizer more than two decades ago and has now developed into one of the most promising research areas in the field of evolutionary computation. The success of DE has been ubiquitously evidenced in various problem domains, e. g., continuous, combinatorial, mixed continuous-discrete, single-objective, multi-objective, constrained, large-scale, multimodal, dynamic and uncertain optimization problems. Furthermore, the remarkable efficacy of DE in real-world applications significantly boosts its popularity.
Over the
past decades, numerous studies on DE have been carried out to improve the
performance of DE, to give a theoretical explanation of the behavior of DE, to
apply DE and its derivatives to solve various scientific and engineering
problems, as demonstrated by a huge number of research publications on DE in the
forms of monographs, edited volumes and archival articles. Consequently, DE
related algorithms have frequently demonstrated superior performance in
challenging tasks. It is worth noting that DE has always been one of the top
performers in previous competitions held at the IEEE Congress on Evolutionary
Computation. Nonetheless, the lack of systematic benchmarking of the DE related
algorithms in different problem domains, the existence of many open problems in
DE, and the emergence of new application areas call for an in-depth
investigation of DE.
This
special session aims at bringing together researchers and practitioners to
review and re-analyze past achievements, to report and discuss latest advances,
and to explore and propose future directions in this rapidly emerging research
area. Authors are invited to submit their original and unpublished work in the
areas including, but not limited to:
New DE variants for continuous, discrete, mixed-variable, single-objective, multi-objective, many-objective, constrained, large-scale, multiple optima seeking (niching), dynamic and uncertain optimization
Review, comparison and analysis of DE in different problem domains
Experimental design and empirical analysis of DE
DE-variants for handling mixed-integer, discrete, and binary optimization problems
Study on initialization, reproduction, and selection strategies in DE
Study on control parameters (e.g., scale factor, crossover rate, and population size) in DE
Self-adaptive and tuning-free DE
Parallel and distributed DE
Theoretical analysis and understanding of DE
Synergy of DE with other soft computing techniques
DE for expensive and surrogate-assisted optimization problems
Hybridization of DE with other optimization techniques
Interactive DE
Deep neuro-evolution and neural architecture search with DE
Application of DE to real-world problems from diverse domains
Important Dates
Paper submission deadline: January 15, 2020
Paper acceptance notification date: March 15, 2020
Final paper submission deadline: April 15, 2020
Please refer to https://wcci2020.org/calls/#call-for-papers for the latest information.
Paper Submission
All papers should be submitted electronically through: https://ieee-cis.org/conferences/cec2020/upload.php
When you submit your papers to our special session, please select "S41. Differential Evolution: Past, Present and Future" as the Main Research Topic*.
Special Session Co-Chairs
Kai Qin
Swinburne University of Technology, Australia
Email: kqin@swin.edu.au
Swagatam Das
Electronics and Communication Sciences Unit, Indian Statistical Institute, India
Email: swagatamdas19@yahoo.co.in
Rammohan Mallipeddi
School of Electronics Engineering, Kyungpook National University, Daegu, South Korea
Email: mallipeddi@knu.ac.kr
Shahryar Rahnamayan
Department of Electrical, Computer, and Software Engineering, University of Ontario Institute of Technology (UOIT), Oshawa, Ontario, Canada
Email: Shahryar.Rahnamayan@uoit.ca
Program Commitee
Andries Engelbrecht, University of Pretoria, South Africa
Carlos A. Coello Coello, CINVESTAV-IPN, México
Daniela Zaharie, West University of Timisoara, Romania
Ferrante Neri, De Montfort University, UK
Janez Brest, University of Maribor, Slovenia
Jing Liang, Zhengzhou University, China
Uday K. Chakraborty, University of Missouri-St. Louis, USA
Ke Tang, University of Science and Technology of China, China
Yong Wang, Central South University, China
Maoguo Gong, Xidian University, China
Mohammad N. Omidvar, University of Birmingham, UK
Ponnuthurai Nagaratnam Suganthan, Nanyang Technological University, Singapore
Wei-neng Chen, South China University of Technology, China
Xiaodong Li, RMIT University, Australia
Zexuan Zhu, Shenzhen University, China
Chuan-Kang Ting, National Chung Cheng University
Aimin Zhou, East China Normal University, China
Several well-recognized test beds recommended for empirical studies:
Competition on Performance Assessment of Multi-Objective Optimization
Algorithms
Competition on Evolutionary Constrained Real-parameter Single-objective
Optimization
Competition on Testing Evolutionary Algorithms on Real-world Numerical
Optimization Problems
Competition on Evolutionary Computation for Dynamic Optimization Problems
Competition on Competition on Niching Methods for Multimodal Optimization