2021 CEC Special Session on “Evolutionary Multitasking”

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 [1-4]. 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.

[1] A. Gupta, Y. S. Ong and L. Feng, “Multifactorial evolution: Toward evolutionary multitasking”, IEEE Transactions on Evolutionary Computation, 20(3):343-357, 2016.
[2] Y. S. Ong and A. Gupta, “Evolutionary multitasking: A computer science view of cognitive multitasking”, Cognitive Computation, 8(2): 125-142, 2016.
[3] Y. S. Ong, "Towards Evolutionary Multitasking: A New Paradigm in Evolutionary Computation", Computational Intelligence, Cyber Security and Computational Models, pp. 25-26, Springer, Singapore, 2016.
[4] K. K. Bali, A. Gupta, Y. S. Ong, and P. S. Tan. "Cognizant Multitasking in MultiObjective Multifactorial Evolution: MO-MFEA-II." IEEE Transactions on Cybernetics, 2020.

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.


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.

Yew-Soon Ong

School of Computer Science and Engineering Nanyang Technological University, Singapore

Email: asysong@ntu.edu.sg

Yew-Soon Ong received the Ph.D. degree on artificial intelligence in complex design from the Computational Engineering and Design Center, University of Southampton, Southampton, U.K., in 2003. He is currently President's Chair Professor of Computer Science at the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He is Director of the Data Science and Artificial Intelligence Research Center, Director of the A*Star SIMTECH-NTU Joint Lab on Complex Systems and Principal Investigator of the Data Analytics & Complex System Programme in the Rolls-Royce@NTU Corporate Lab. Dr. Ong is founding Editor-In-Chief of the IEEE Transactions on Emerging Topics in Computational Intelligence, Associate Editor of IEEE Transactions on Evolutionary Computation, IEEE Transactions on Neural Network & Learning Systems, IEEE Transactions on Cybernetics, and others.