Supporting CEC2021 Competition on Evolutionary Transfer Multiobjective Optimization
28 June - 01 July 2021, Krakow, Poland
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Authors are invited to submit their original and unpublished work to this special session.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:
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. liangf@cqu.edu.cn Phone: +86-23-65102502
Yew-Soon Ong School of Computer Science and Engineering, Nanyang Technological University, Singapore. asysong@ntu.edu.sg 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, Hung Hom, Kowloon, Hong Kong SAR kctan@comp.polyu.edu.hk Phone: (852) 2766 7271
Bing Xue is currently an Associate Professor and Program Director of Science in School of Engineering and Computer Science at VUW. She has over 200 papers published in fully refereed international journals and conferences and her research focuses mainly on evolutionary computation, machine learning, classification, symbolic regression, feature selection, evolving deep neural networks, image analysis, transfer learning, multi-objective machine learning. Dr Xue is currently the Chair of IEEE Computational Intelligence Society (CIS) Data Mining and Big Data Analytics Technical Committee, and Vice-Chair of IEEE Task Force on Evolutionary Feature Selection and Construction, Vice-Chair of IEEE CIS Task Force on Transfer Learning & Transfer Optimization, and of IEEE CIS Task Force on Evolutionary Deep Learning and Applications. A/Prof Xue is the organiser of the special session on Evolutionary Feature Selection and Construction in IEEE Congress on Evolutionary Computation (CEC) 2015, 2016, 2017, 2018 2019, and 2020. A/Prof Xue has been a chair for a number of international conferences including the Chair of Women@GECCO 2018 and a co-Chair of the Evolutionary Machine Learning Track for GECCO 2019 and 2020. She is the Lead Chair of IEEE Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition (FASLIP) at SSCI 2016, 2017,2018, 2019 and 2020, a Program Co-Chair of the 7th International Conference on Soft Computing and Pattern Recognition (SoCPaR2015), a Program Chair of the 31th Australasian Joint Conference on Artificial Intelligence (AI 2018), and Finance Chair for 2019 IEEE Congress on Evolutionary Computation.
She is an Associate Editor or Member of the Editorial Board for seven international journals, including IEEE Transactions of 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 an Assistant 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 and Professor (Cross Appointment) of the School of Physical and Mathematical Science at Nanyang Technological University (NTU), Singapore. At the same time, he is 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 Transactions on Emerging Topics in Computational Intelligence, Associate Editor of IEEE Transactions on Evolutionary Computation, IEEE Transactions on Neural Network & Learning Systems, and IEEE Transactions on Cybernetics, 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 Computational Intelligence Magazine Outstanding Paper Award, the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and the 2019 IEEE Transactions on Evolutionary Computation 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 is a member of the University Academic Board, a member of the University Postgraduate Scholarships Committee, Associate Dean (Research and Innovation) in the Faculty of Engineering, and Chair of the Research Committee of the Faculty of Engineering and School of Engineering and Computer Science.
His research is mainly focused on evolutionary computation, particularly genetic programming, particle swarm optimisation and learning classifier systems with application areas of feature selection/construction and dimensionality reduction, computer vision and image processing, evolutionary deep learning and transfer learning, job shop scheduling, multi-objective optimisation, and clustering and classification with unbalanced and missing data. He is also interested in data mining, machine learning, and web information extraction. Prof Zhang has published over 500 research papers in refereed international journals and conferences in these areas.
He has been serving as an associated editor or editorial board member for over 10 international journals including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, the Evolutionary Computation Journal (MIT Press), ACM Transactions on Evolutionary Learning and Optimisation, Genetic Programming and Evolvable Machines (Springer), IEEE Transactions on Emergent Topics in Computational Intelligence, Applied Soft Computing, and Engineering Applications of Artificial Intelligence, and as a reviewer of over 30 international journals. He has been a major chair for eight international conferences. He has also been serving as a steering committee member and a program committee member for over 80 international conferences including all major conferences in evolutionary computation. Since 2007, he has been listed as one of the top ten world genetic programming researchers by the GP bibliography (http://www.cs.bham.ac.uk/~wbl/biblio/gp-html/index.html).
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). Since 2014, he has been co-organising and co-chairing the special session on evolutionary feature selection and construction at IEEE CEC and SEAL, 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 Zhang was the Chair of the IEEE CIS Intelligent Systems Applications, the IEEE CIS Emergent Technologies Technical Committee, and the IEEE CIS Evolutionary Computation Technical Committee; a Vice-Chair of the IEEE CIS Task Force on Evolutionary Feature Selection and Construction, the IEEE CIS Task Force on Evolutionary Computer Vision and Image Processing, and the IEEE CIS Task Force on Evolutionary Deep Learning and Applications; and also the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.
Prof. Kay Chen Tan is 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 holds one U.S. patent on surface defect detection, and another one is pending approval.
Prof. Tan is currently the Vice-President (Publications) of IEEE Computational Intelligence Society, USA. He has served as the Editor-in-Chief of IEEE Transactions on Evolutionary Computation from 2015-2020 (IF: 11.169) and IEEE Computational Intelligence Magazine from 2010-2013 (IF: 9.083). Prof. Tan currently serves as an Associate Editor of various international journals, such as IEEE Transactions on Artificial Intelligence, IEEE Transactions on Cybernetics, and IEEE Transactions on Games.
Prof. Tan has been invited as a Plenary/Keynote speaker for over 80 international conferences, including the 2020 IEEE World Congress on Computational Intelligence, the 2016 IEEE Symposium Series on Computational Intelligence, etc. He has served as an organizing committee Chair/Co-Chair for over 50 international conferences, including the General Co-Chair of 2019 IEEE Congress on Evolutionary Computation, and the General Co-Chair of 2016 IEEE World Congress on Computational Intelligence, etc.
Prof. Tan has received a number of research awards, such as the 2020 IEEE Transactions on Cybernetics Outstanding Paper Awards, the 2019 IEEE Computational Intelligence Magazine Outstanding Paper Awards, the 2016 IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Awards, the 2012 Outstanding Early Career Award presented by the IEEE Computational Intelligence Society, and the 2008 Recognition Award given by the International Network for Engineering Education & Research.
Prof. Tan is an IEEE Fellow, 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.