CEC-2024 Special Session on "Evolutionary Multitasking"

Organized by Yaqing Hou, Kai Qin, Liang Feng, Abhishek Gupta, Yew-Soon Ong

Contact email: houyq@dlut.edu.cn, kqin@swin.edu.au, liangf@cqu.edu.cn, abhishek_gupta@simtech.a-star.edu.sg, asysong@ntu.edu.sg

Website: https://wcci2024.org/

Session abstract
   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 [1-5]. 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.

[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 Multi-Objective Multifactorial Evolution: MO-MFEA-II." IEEE Transactions on Cybernetics, 2020.
[5] Gupta, A., Zhou, L., Ong, Y. S., Chen, Z., & Hou, Y. "Half a dozen real-world applications of evolutionary multitasking, and more." IEEE Computational Intelligence Magazine 17.2 (2022): 49-66.

Keywords:
Evolutionary Multitasking;
Multi-task Optimization;
Evolutionary Computation;
Transfer Optimisation

Acknowledgement
This work is supported by IEEE CIS Intelligent System Application Technical Committee (ISATC), task force on Transfer Learning & Transfer Optimization.

Session description
Objectives, goals:
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. Authors are invited to submit their original and unpublished work to this special session. 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
  ● Transfer optimization for real-world applications
  ● Etc.

Short biographical sketch for each organizer
Yaqing Hou received the Ph.D. degree in artificial intelligence from Interdisciplinary Graduate School, Nanyang Technological University, Singapore, in 2017. He was a Postdoctoral Research Fellow with Data Science and Artificial Intelligence Research Centre, Nanyang Technological University. He is currently an Associate Professor with the School of Computer Science and Technology, Dalian University of Technology, Dalian, China. His research interests include computational and artificial intelligence, multi-agent learning systems, evolutionary multi-tasking and transfer optimization. He is now an Associate Editor of the IEEE Transactions on Cognitive and Developmental Systems, Memetic Computing. He had co-organized multiple Special Issues on Memetic Computing journal and Applied Soft computing.

Kai Qin is a Professor at Swinburne University of Technology, Melbourne, Australia. Currently, he is the Director of Swinburne Intelligent Data Analytics Lab and the Deputy Director of Swinburne Space Technology and Industry Institute. Before joining Swinburne, he worked at Nanyang Technological University (Singapore), the University of Waterloo (Canada), INRIA Grenoble Rhône-Alpes (France), and RMIT University (Australia). His major research interests include machine learning, evolutionary computation, collaborative learning and optimization, computer vision, remote sensing, services computing, and edge computing. He was a recipient of the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award and the 2022 IEEE Transactions on Neural Networks and Learning Systems Outstanding Associate Editor. He is currently the Chair of the IEEE Computational Intelligence Society (CIS) Student Activities and Young Professionals Sub-committee, the Vice-Chair of the IEEE CIS Neural Networks Technical Committee, the Vice-Chair of the IEEE CIS Emergent Technologies Task Force on “Multitask Learning and Multitask Optimization”, the Vice-Chair of the IEEE CIS Neural Networks Task Force on “Deep Edge Intelligence, and the Chair of the IEEE CIS Neural Networks Task Force on “Deep Vision in Space”. He serves as the Associate Editor for several top-tier journals, e.g., IEEE TEVC, IEEE TNNLS, IEEE CIM, NNs, and SWEVO. He was the General Co-Chair of the 2022 IEEE International Joint Conference on Neural Networks (IJCNN 2022) held in Padua, Italy, and was the Chair of the IEEE CIS Neural Networks Technical Committee during the 2021-2022 term.

Liang Feng received his Ph.D degree from the School of Computer Engineering, Nanyang Technological University, Singapore, in 2014. He was a Postdoctoral Research Fellow at the Computational Intelligence Graduate Lab, Nanyang Technological University, Singapore. 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. He has been honored with the 2019 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, the 2023 IEEE TETCI Outstanding Paper Award, and the 2024 IEEE CIM Outstanding Paper Award. He is an Associate Editor of the IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, IEEE CIM, and Memetic Computing. He is also the founding Chair of the IEEE CIS Intelligent Systems Applications Technical Committee Task Force on “Transfer Learning & Transfer Optimization” and the PC member of the IEEE Task Force on “Memetic Computing”. He had co-organized and chaired the Special Session on “Memetic Computing” and “Evolutionary Transfer Learning and Transfer Optimisation” held at IEEE CEC since 2016.

Abhishek Gupta received the Ph.D degree in Engineering Science from the University of Auckland, New Zealand, in 2014. Over the past 5 years, Dr. Gupta has been working in the area of Memetic Computation, with particular focus on developing novel theories and algorithms in the topics of evolutionary transfer and multitask optimization. His pioneering work on evolutionary multitasking, in particular, was bestowed the 2019 IEEE Transactions on Evolutionary Computation Outstanding Paper Award by the IEEE Computational Intelligence Society (CIS). He is Associate Editor of the IEEE Transactions on Emerging Topics in Computational Intelligence, and is also the founding Chair of the IEEE CIS Emergent Technology Technical Committee (ETTC) Task Force on Multitask Learning and Multitask Optimization. He is currently appointed as a Scientist in the Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR). He also jointly serves as an Adjunct Research Scientist in the Data Science and Artificial Intelligence Research Center, School of Computer Science and Engineering, Nanyang Technological University, Singapore.

Yew-Soon Ong is an IEEE Fellow and is currently President's Chair Professor of Computer Science at the School of Computer Science and Engineering at Nanyang Technological University (NTU), Singapore. He is also Chief Artificial Intelligence (CAS) Scientist of the Singapore's Agency for Science, Technology and Research (A*STAR). At NTU, he serves as Director of the Data Science and Artificial Intelligence Research Center (DSAIR), co-Director of the Singtel-NTU Cognitive & Artificial Intelligence Joint Lab (SCALE@NTU), co-Director of the A*Star SIMTECH-NTU Joint Lab on Complex Systems. Prof. Ong is founding Editor-In-Chief of the IEEE TETCI, Associate Editor of IEEE TEVC, IEEE TNNLS, IEEE TCYB, and others. His research interests in computational intelligence span across memetic computation, complex design optimization, intelligent agents and Big Data Analytics. He received the 2015 IEEE CIM Outstanding Paper Award, the 2012 IEEE TEVC Outstanding Paper Award, and the 2019 IEEE TEVC Outstanding Paper Award.