18 - 23 July 2022, Padova, Italy
Evolutionary computation includes a group of nature-inspired population-based techniques, which have been successfully applied to many complex learning and optimisation problems, such as classification, clustering, vehicle routing, job shop scheduling, and face recognition. Most evolutionary learning and optimisation methods discard knowledge gained while solving one problem. When given a new problem, an evolutionary learning or optimization method will start from scratch, regardless how similar the new problem is to the already addressed problems. However, many real-world problems are closely related, so experience or knowledge learnt from solving one problem could very helpful when solving another problem, such as knowledge learnt from texture image classification can be helpful for brain tumor detection using MRI images. In machine learning, transfer learning aims to transfer knowledge acquired in one problem domain, i.e. the source domain, onto another domain, i.e. the target domain. Transfer learning is now hot topic in data mining and machine learning, which has attracted increasing attention from many disciplines. In recent years, there is a growing interest in utilizing transfer knowledge in evolutionary computation to address challenging learning and optimisation tasks.
The theme of this special session is evolutionary transfer learning and transfer optimisation , covering ALL different evolutionary computation paradigms, including Genetic algorithms (GAs), Genetic programming (GP), Evolutionary programming (EP), Evolution strategies (ES), Learning classifier systems (LCS), Particle swarm optimization (PSO), Ant colony optimization (ACO), Differential evolution (DE), Evolutionary Multi-objective optimization (EMO) and Memetic computing (MC).
The aim is to investigate in both the new theories and methods on knowledge transfer can be achieved with different evolutionary computation paradigms, and the applications of evolutionary transfer learning and transfer optimisation in real-world problems.
Authors are invited to submit their original and unpublished work to this special session. Topics of interest include but are not limited to:
This special session is supported by IEEE CIS Task Force on Transfer Learning & Transfer Optimization from ISATC, and IEEE CIS Task Force on Evolutionary Computation for Feature Selection and Construction from ECTC.
Bing Xue School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand. Bing.Xue@ecs.vuw.ac.nz Phone: +64-4-463 5542; Fax: +64-4-463 5045.
Liang Feng College of Computer Science, Chongqing University, China. firstname.lastname@example.org Phone: +86-23-65102502
Yew-Soon Ong School of Computer Science and Engineering, Nanyang Technological University, Singapore. email@example.com Phone: +65-6790-5778, Fax: +65-6792-6559
Mengjie Zhang School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand. Mengjie.Zhang@ecs.vuw.ac.nz Phone: +64-4-463 5654; Fax: +64-4-463 5045
Kay Chen Tan Department of Computing, The Hong Kong Polytechnic University firstname.lastname@example.org Phone: (852) 27667271
Bing Xue is currently a Professor in Artificial Intelligence and Program Director of Science in the School of Engineering and Computer Science at Victoria University of Wellington. She has over 300 papers published in fully refereed international journals and conferences. She is currently the Chair of IEEE CIS Task Force on Transfer Learning & Transfer Optimization. She is an Associate Editor of several international journals, including IEEE TEVC. Prof Xue organised many special sessions and symposiums in international conferences such as IEEE WCCI/CEC, IEEE SSCI, and ACM GECCO. She has been a chair for many international conferences including program chair for SoCPaR2015 and Australasian AI 2018, finance chair for IEEE CEC 2019, general co-chair for IVCNZ 2020, workshop co-chair for IEEE ICDM 2021, and tutorial co-chair for WCCI 2022. She is an Associate Editor or Member of the Editorial Board for seven international journals, including IEEE Transactions on Evolutionary Computation, IEEE Computational Intelligence Magazine, and ACM Transactions on Evolutionary Learning and Optimisation.
Liang Feng received the PhD 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.
Prof. 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.
Prof. Mengjie Zhang is a Fellow of Royal Society of New Zealand, a Fellow of IEEE, and currently Professor of Computer Science at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation Research Group. He has been serving as an associated editor or editorial board member for over 10 international journals including IEEE Transactions on Evolutionary Computation, and IEEE Transactions on Cybernetics He is the Tutorial Chair for GECCO 2014, an AIS-BIO Track Chair for GECCO 2016, an EML Track Chair for GECCO 2017, and a GP Track Chair for GECCO 2020. Since 2012, he has been co-chairing several parts of IEEE CEC, SSCI, and EvoIASP/EvoApplications conference (he has been involving major EC conferences such as GECCO, CEC, EvoStar, SEAL). He has been co-organising and co-chairing many special sessions, and also delivered a keynote/plenary talk for IEEE CEC 2018, IEEE ICAVSS 2018, DOCSA 2019, IES 2017 and Chinese National Conference on AI in Law 2017.
Prof. Kay Chen Tan is an IEEE Fellow, and currently a Chair Professor (Computational Intelligence) of the Department of Computing, the Hong Kong Polytechnic University. He has co-authored 7 books and published over 200 peer-reviewed journal articles. Prof. Tan is currently the Vice-President (Publications) of IEEE Computational Intelligence Society, USA. He has received many research awards, such as the 2020 IEEE TCYB Outstanding Paper Awards. has been invited as a Plenary/Keynote speaker for over 80 international conferences and served as an organizing committee Chair/Co-Chair for over 50 international conferences, such as general co-chair of IEEE WCCI 2016 and general co-chair of IEEE CEC 2019. Prof. Tan is an IEEE Distinguished Lecturer Program (DLP) speaker since 2012, and an Honorary Professor at University of Nottingham in UK since 2020. Prof. Tan is also the Chief Co-Editor of Springer Book Series on Machine Learning: Foundations, Methodologies, and Applications launched in 2020.