Donostia - San Sebastián, Spain, June 5-8, 2017
Data mining, machine learning, and optimisation algorithms have achieved promises in many real-world tasks, such as classification, clustering and regression. These algorithms can often generalise well on data in the same domain, i.e. drawn from the same feature space and with the same distribution. However, in many real-world applications, the available data are often from different domains. For example, we may need to perform classification in one target domain, but only have sufficient training data in another (source) domain, which may be in a different feature space or follow a different data distribution. 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 has recently emerged as a new learning framework and hot topic in data mining and machine learning.
Evolutionary computation techniques have been successfully applied to many real-world problems, and started to be used to solve transfer learning tasks. Meanwhile, transfer learning has attracted increasing attention from many disciplines, and has been used in evolutionary computation to address complex and challenging issues. The theme of this special session is transfer learning in evolutionary computation, 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), and Evolutionary Multi-objective optimization (EMO).
The aim is to investigate in both the new theories and methods on how transfer learning can be achieved with different evolutionary computation paradigms, and how transfer learning can be adopted in evolutionary computation, and the applications of evolutionary computation and transfer learning 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 Authors are invited to submit their original and unpublished work to this special session.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:
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 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.
Harith Al-Sahaf School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand. Harith.firstname.lastname@example.org Phone: +64 4 463 5661; Fax: +64-4-463 5045.
Yi Mei School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand. Yi.Mei@ecs.vuw.ac.nz Phone: +64-4-463 5233+8016; Fax: +64-4-463 5045.
Dr Mengjie Zhang is 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, a member of the Faculty of Graduate Research Board at the University, 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, job shop scheduling, multi-objective optimisation, 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 400 research papers in refereed international journals and conferences in these areas. He has been serving as an associated editor or editorial board member for seven international journals including IEEE Transactions on Evolutionary Computation, the Evolutionary Computation Journal (MIT Press), IEEE Transactions on Emergent Technologies in Computational Intelligence, Genetic Programming and Evolvable Machines (Springer), Applied Soft Computing, IEEE Transactions on Emergent Topics in Computational Intelligence, Natural Computing, and Engineering Applications of Artificial Intelligence, and as a reviewer of over 30 international journals. He has been involving major EC conferences such as GECCO, IEEE CEC, EvoStar, 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 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).
Prof Zhang is the Chair of the IEEE Emergent Technologies Technical Committee, the immediate Past Chair of 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, and the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.
Bing Xue is currently a lecturer and co-leader of the Evolutionary Computation Research Group, School of Engineering and Computer Science at Victoria University of Wellington, and leading the strategic research direction on evolutionary feature selection and construction. Her research focuses mainly on evolutionary computation, pattern recognition, feature selection, feature extraction, feature construction, multi-objective optimisation, data mining and machine learning. She has over 70 papers published in fully referred international journals and conferences and most of them are on evolutionary feature selection and construction. She is currently co-supervising over 10 PhD and Master's students and visiting scholars, and over 10 Honours and summer research projects.
Dr Xue is currently the Chair of the IEEE Task Force on Evolutionary Feature Selection and Construction, consisting of over 20 members for the five continents working in this area. She is the main Chair of IEEE Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition (FASLIP) in IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), the main organiser of the special session on Evolutionary Feature Selection and Construction in IEEE Congress on Evolutionary Computation (CEC) 2015 and WCCI/CEC 2016. She is also a program co-chair of the 7th International Conference on Soft Computing and Pattern Recognition (SoCPaR2015), Publicity Chair for the Australian Conference on Artificial Lift and Computational Intelligence (ACALCI 2017), Special Session co-Chair for The 20th Asia-Pacific Symposium on Intelligent and Evolutionary Systems (IES2016), Special Session Co-chair on Evolutionary Feature Reduction in the international conference on Simulated Evolution And Learning (SEAL 2014). She is a member of Editorial Board for Applied Soft Computing (journal), International Journal of Computer Information Systems and Industrial Management Applications and International Journal of Swarm Intelligence Research, and also a Guest Editor for the Special Issue on Evolutionary Feature Reduction and Machine Learning for the Springer Journal of Soft Computing. Dr Xue is serving as a reviewer of over 10 international journals including IEEE Transactions on Evolutionary Computation, IEEE Transaction on Cybernetics and Information Sciences. She is a program committee member for many international conferences including Genetic and Evolutionary Computation Conference (GECCO), European Joint Conference on Evolutionary Computation (EvoStar -- EuroGP, EvoCOP and EvoApplications), IEEE Congress on Evolutionary Computation (CEC), International Joint Conference on Artificial Intelligence (IJCAI), Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), and International Conference on Simulated Evolution and Learning (SEAL). She is also serving as the Director of Women in Engineering for the IEEE New Zealand Central Section and the Secretary of the IEEE Chapter on Computational Intelligence in that Section.
Harith Al-Sahaf is currently with the School of Engineering and Computer Science at Victoria University of Wellington. His research interests include evolutionary computation, computer vision, pattern recognition, machine learning, feature manipulation including feature detection, selection, extraction and construction, transfer learning, domain adaptation, one-shot learning, and image understanding. He is co-leading the Evolutionary Computer Vision and Image Analysis strategic direction at the Evolutionary Computation Research Group.
Harith has published over 15 papers in fully refereed international journals and conferences, including the top EC journals such as IEEE Transaction in Evolutionary Computation (IEEE TEVC) and Evolutionary Computation (MIT Press), and top EC conferences including Genetic and Evolutionary Computation Conference (GECCO), IEEE Congress on Evolutionary Computation (CEC), European conference on Genetic Programming (EuroGP). He received the overall best paper award of 2015 IEEE CEC: Image Descriptor: A Genetic Programming Approach to Multiclass Texture Classification (over 700 submissions). He won the 1st prize of the 2015 IEEE Postgraduate Presentation Day event. Harith is a member of the IEEE Computational Intelligence Society (CIS), and a member of the IEEE CIS Task Force on Evolutionary Computation for Feature Selection and Construction. He has been serving as a reviewer for top international journals including IEEE TEVC, Soft Computing, Complex and Intelligent System, Applied Soft Computing, Information Science, and Journal of Electronic Imaging; and is a program committee member for a number of international conferences such as IEEE CEC, SSCI, AI, SoCPaR, ADMA, and IVCNZ.
Dr Yi Mei (S'09-M'13) is a Lecturer at the School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand. His research interests include evolutionary computation in scheduling, routing and combinatorial optimisation, as well as evolutionary machine learning, genetic programming, feature selection and dimensional reduction. Yi has almost 40 publications, including the top journals in EC and Operations Research (OR) such as IEEE TEVC, IEEE Transactions on Cybernetics, European Journal of Operational Research, ACM Transactions on Mathematical Software, and top EC conferences (GECCO). As the sole investigator, he won the 2nd prize of the Competition at IEEE WCCI 2014: Optimisation of Problems with Multiple Interdependent Components. He received the 2010 Chinese Academy of Sciences Dean’s Award (top 200 postgraduates all over China) and the 2009 IEEE Computational Intelligence Society (CIS) Postgraduate Summer Research Grant (three to four recipients all over the world). Yi is serving as the committee member of IEEE ECTC Task Force on Evolutionary Scheduling and Combinatorial Optimisation, IEEE CIS Task Force on EC for Feature Selection and Construction and IEEE CIS Task Force on Large Scale Global Optimisation. He is a guest editor of the Genetic Programming Evolvable Machine journal, and co-chair of a number of special sessions in international conferences such as IEEE CEC. He is serving as a reviewer of over 20 international journals including the top journals in EC and OR and PC member of almost 20 international conferences.