Evolutionary multitasking is an emerging concept in computational intelligence that realizes the theme of efficient multi-task problem-solving in the domain of numerical optimization. It is worth noting that in the natural world, the process of evolution has, in a single run, successfully produced diverse living organisms that are skilled at survival in a variety of ecological niches. In other words, the process of evolution can itself be thought of as a massive multi-task engine where each niche forms a task in an otherwise complex multifaceted fitness landscape, and the population of all living organisms is simultaneously evolving to survive in one niche or the other. Interestingly, it may happen that the genetic material evolved for one task is effective for another as well, in which case the scope for inter-task genetic transfers facilitates frequent leaps in the evolutionary progression towards superior individuals. Being nature-inspired optimization procedures, it has recently been shown that evolutionary algorithms (EAs) are not only equipped to mimic Darwinian principles of “survival-of-the-fittest”, but their reproduction operators are also capable of inducing the afore-stated inter-task genetic transfers in multitask optimization settings; although, the practical implications of the latter are yet to be fully studied and exploited in the literature.

The aim of this special session is to provide a forum for researchers in this field to exchange the latest advances in theories, technologies, and practice of evolutionary multitasking.

This special session is supported by IEEE CIS Task Force on Transfer Learning & Transfer Optimization from ISATC, and IEEE CIS Task Force on Multitask Learning and Multitask Optimization from ETTC.

Scope and Topics

The scope of this special session covers, but is not limited to:
  • Implicit or explicit evolutionary multitasking for continuous or combinatorial optimization
  • Implicit or explicit evolutionary multitasking with adaptive knowledge transfer schemes
  • Computational resource allocation in evolutionary multitasking
  • Evolutionary multitasking for large-scale, expensive, and complex optimization
  • Multi-form optimization via evolutionary multitasking
  • Evolutionary multitasking for cloud-based optimization service
  • Theoretical studies that enhance our understandings on the behaviors of evolutionary multitasking
  • Evolutionary multitasking in cases having large number of tasks
  • GPU based evolutionary multitasking
  • Performance evaluation in evolutionary multitasking
  • Evolutionary multitasking for real-world applications
  • Etc.

Important Dates

The scope of this special session covers, but is not limited to:
  • Special Session and Workshop proposals: November 15, 2021
  • Competition and Tutorial proposals: December 13, 2021
  • Paper submission: January 31, 2022 (11:59 PM AoE) STRICT DEADLINE
  • Notification of acceptance: April 26, 2022
  • Final paper submission: May 23, 2022


Liang Feng

Chongqing University, China.

Email: liangf@cqu.edu.cn

Liang Feng received the Ph.D degree from the School of Computer Engineering, Nanyang Technological University, Singapore, in 2014. He is currently a Professor at the College of Computer Science, Chongqing University, China. His research interests include Computational and Artificial Intelligence, Memetic Computing, Big Data Optimization and Learning, as well as Transfer Learning. His research work on evolutionary multitasking won the 2019. IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is Associate Editor of the IEEE Computational Intelligence Magazine, Memetic Computing, and Cognitive Computation. He is also the founding Chair of the IEEE CIS Intelligent Systems Applications Technical Committee Task Force on “Transfer Learning & Transfer Optimization”.

Chuan-Kang Ting

National Tsing Hua University, Taiwan

Email: ckting@pme.nthu.edu.tw

Chuan-Kang Ting received Dr. rer. nat. degree in Computer Science from Paderborn University, Germany. He is currently a Professor and the Chair of Department of Power Mechanical Engineering, National Tsing Hua University, Taiwan. His research interests include evolutionary computation, artificial intelligence, machine learning, and their applications in machinery, manufacturing, ethics, music and arts. Dr. Ting is the Editor-inChief of IEEE Computational Intelligence Magazine (IEEE) and Memetic Computing (Springer). He is an Associate Editor of the IEEE Transactions on Emerging Topics in Computational Intelligence and an Editorial Board Member of Soft Computing. He serves as the Chair of IEEE CIS Creative Intelligence Task Force. He was the Special Session Chair of IEEE WCCI 2016, WCCI 2018, and CEC 2019, Chair of IEEE Symposium on Computational Intelligence for Creativity and Affective Computing 2013, Program Chair of TAAI (2012, 2015, 2019), and Organizing Chair of AI Forum 2012.

Kai Qin

Department of Computer Science and Software Engineering Swinburne University of Technology, Australia

Email: kqin@swin.edu.au

Kai Qin is a professor of Department of Computer Science and Software Engineering Swinburne University of Technology, Australia. He received the PhD degree at Nanyang Technology University (Singapore) in 2007. From 2007 to 2009, he worked as a Postdoctoral Fellow at the University of Waterloo (Waterloo, Canada). From 2010 to 2012, he worked at INRIA (Grenoble, France), first as a Postdoctoral Researcher and then as an Expert Engineer. He joined RMIT University in 2012 as a Vice-Chancellor’s Research Fellow, and then worked as a Lecturer between 2013 and 2016 and a Senior Lecturer from 2017. His major research interests include evolutionary computation, machine learning, computer vision, GPU computing and services computing. Two of his authored/coauthored journal papers have become the 1st and 4th most-cited papers among all of the papers published in the IEEE Transactions on Evolutionary Computation (TEVC) over the last 10 years according to the Web of Science Essential Science Indicators. He is the recipient of the 2012 IEEE TEVC Outstanding Paper Award. One of his conference papers was nominated for the best paper at the 2012 Genetic and Evolutionary Computation Conference (GECCO’12). He won the Overall Best Paper Award at the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES’14). He is serving as the Chair of the IEEE Emergent Technologies Task Force on “Collaborative Learning and Optimization”, promoting the emerging research of the synergy between machine learning and optimization. He had coorganized and chaired the Special Session on “Differential Evolution: Past, Present and Future” held at CEC’12, CEC’13, CEC’14, CEC’15, CEC’16 and CEC’17.