1-5 July 2023, Chicago, USA
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Authors are invited to submit their original and unpublished work to this special session.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. email@example.com Phone: +86-23-65102502
Yew-Soon Ong School of Computer Science and Engineering, Nanyang Technological University, Singapore. firstname.lastname@example.org 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
Bing Xue is currently Professor of Artificial Intelligence, and Deputy Head of School in the School of Engineering and Computer Science at VUW. Her research focuses mainly on evolutionary computation, machine learning, big data, feature selection/learning, evolving neural networks, explainable AI and their real-world applications. Bing has over 300 papers published in fully refereed international journals and conferences including many highly cited papers and top most popular papers. Bing is currently the Editor of IEEE CIS Newsletter, Chair of the Evolutionary Computation Technical Committee, member of ACM SIGEVO Executive Committee and Chair of IEEE CIS Task Force on Evolutionary Deep Learning and Applications. Bing has also been served as an Associate/Guest Editor or Editorial Board Member for > 10 international journals, including IEEE TEVC, ACM TELO, IEEE TETCI, IEEE TAI, and IEEE CIM. She is a key organiser for many international conferences, e.g. Conference Chair of IEEE CEC 2024, Co-ambassador for Women in Data Science NZ 2023, Panel Chair and Conflict-of-Interest Chair for IEEE CEC 2023, Tutorial Chair for IEEE WCCI 2022, Publication Chair of EuroGP 2022, Track Chair for ACM GECCO 2019-2022, Workshop Chair for IEEE ICDM 2021, Conference Activities Chair for IEEE SSCI 2021, Publicity Chair for IEEE CEC 2021, General Co-Chair of IVCNZ 2020, Program Co-Chair for KETO 2020, Senior PC of IJCAI 2019-2021, Finance Chair of IEEE CEC 2019, Program Chair of Austrasia AI 2018, IEEE CIS FASLIP Symposium founder and Chair since 2016, and others in international conferences.
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. 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” 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.
Prof. Yew-Soon Ong is an IEEE Fellow, and currently President's Chair Professor of Computer Science at the School of Computer Science and Engineering 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 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. He is a the 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 Artificial Intelligence, and chief co-editor of Book Series on Studies in Adaptation, Learning, and Optimization. His current research interests include Artificial & Computational Intelligence spanning Memetic Computation, Evolutionary & Transfer Optimization and Machine Learning. His research grants comprises of external funding from both national and international partners that include Boeing Research & Development (USA), Rolls-Royce (UK) and Honda Research Institute Europe (Germany), the National Research Foundation of Singapore, National Grid Office, A*STAR, Singapore Technologies Dynamics and MDA-GAMBIT. His research on Memetic Computation was first featured by Thomson Scientific's Essential Science Indicators as one of the most cited emerging area of research in August 2007. He was listed as a Thomson Reuters Highly Cited Researcher in 2015 and 2016 and among the World's Most Influential Scientific Minds. He also received the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, the 2015 IEEE Computational Intelligence Magazine Outstanding Paper Award and recently the 2019 IEEE Transactions on Evolutionary Computation Outstanding Paper Award for his works in Memetic Computation.
Prof. Mengjie Zhang is a Fellow of Royal Society of New Zealand, a Fellow of Engineering New Zealand, a Fellow of IEEE, an IEEE Distinguished Lecturer, currently Professor of Computer Science at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation and Machine Learning Research Group. He is a member of the University Academic Board, Associate Dean (Research and Innovation) for the Faculty of Engineering, and Chair of the Research Committee of the Faculty of Engineering. His research is mainly focused on artificial intelligence (AI), machine learning and big data, feature selection/construction and big dimensionality reduction, computer vision and image processing, job shop scheduling and resource allocation, multi-objective optimisation, and evolutionary deep learning and transfer learning. Prof Zhang has published over 800 research papers in refereed international journals and conferences in these areas. He has been serving as an associated editor for over ten international journals including IEEE Transactions on Evolutionary Computation, and IEEE Transactions on Cybernetics. He has been involving major AI and EC conferences such as GECCO, IEEE CEC, EvoStar, IJCAI, PRICAI, PAKDD, AusAI, IEEE SSCI and SEAL as a Chair. He has also been serving as a steering committee member and a program committee member for over 100 international conferences. Prof Zhang is a past Chair of the IEEE CIS Intelligent Systems Applications Technical Committee, 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, a vice-chair of the IEEE CIS Task Force on Evolutionary Computer Vision and Image Processing. He is currently the Chair of the IEEE CIS PubsCom Strategic Planning Committee and the IEEE CIS Outstanding PhD Dissertation Award Committee, and the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.