Monday, 5 October 2015

Call for Papers Special Session on Transfer Learning in Evolutionary Computation

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.

Aim and Scope: 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), Artificial immune systems (AIS), Evolutionary Multi-objective optimization (EMO), Estimation of distribution algorithms (EDA), and Cultural algorithms (CA).

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 Authors are invited to submit their original and unpublished work to this special session.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:
  • Evolutionary supervised transfer learning
  • Evolutionary unsupervised transfer learning
  • Evolutionary semi-supervised transfer learning
  • Domain adaptation and domain generalization in evolutionary computation
  • Instance based transfer approaches in evolutionary computation
  • Feature based transfer learning in evolutionary computation
  • Parameter/model based transfer learning in evolutionary computation
  • Relational based transfer learning in evolutionary computation
  • Transfer learning in in evolutionary computation for classification
  • Transfer learning in in evolutionary computation for regression
  • Transfer learning in in evolutionary computation for clustering
  • Transfer learning in in evolutionary computation for other data mining tasks, such as association rules and link analysis
  • Transfer learning in in evolutionary computation for scheduling and combinatorial optimisation tasks
  • Hybridisation of evolutionary computation and neural networks, and fuzzy systems for transfer learning
  • Hybridisation of evolutionary computation and machine learning, information theory, statistics, etc., for transfer learning 
  • Transfer learning in in evolutionary computation for real-world applications, e.g. text mining, image analysis, face recognition, WiFi localisation, et al.

Important dates:

  • Paper Submission Deadline: 15 Jan 2016
  • Notification of Acceptance: 15 Mar 2016
  • Final Paper Submission Deadline: 15 Apr 2016

Paper Submission:

Please follow the IEEE WCCI/CEC 2016 Submission Web Site. Special session papers are treated the same as regular conference papers. Please specify that your paper is for the Special Session on Evolutionary Feature Selection and Construction. All papers accepted and presented at WCCI/CEC 2016 will be included in the conference proceedings published by IEEE Explore, which are typically indexed by EI.

Organizers:

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

Muhammad Iqbal
School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand.
Muhammad.Iqbal@ecs.vuw.ac.nz; Phone: +64-4-463 5233+8874; 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+8874; 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.

Biography of the Organisers

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 University Research Committee, Associate Dean (Research and Innovation) for Faculty of Engineering, and Chair of the Research Committee of the Faculty and School of Engineering and Computer Science. His research is mainly focused on evolutionary computation, particularly genetic programming, particle swarm optimisation, multi-objective optimisation and learning classifier systems with application areas of classification with unbalanced data, feature selection and dimensionality reduction, computer vision and image processing, job shop scheduling, and bioinformatics. Recently, he has been working on transfer learning, domain adaptation and domain generalization in evolutionary computation and neural networks for classification, regression, scheduling and routing, and computer vision and image processing problems.  He is also interested in data mining, machine learning, and web information extraction. Prof Zhang has published over 350 academic papers in refereed international journals and conferences in these areas. He has been serving as an associated editor or editorial board member for five international journals including IEEE Transactions on Evolutionary Computation, the Evolutionary Computation Journal (MIT Press) and Genetic Programming and Evolvable Machines (Springer), and as a reviewer of over 20 international journals. He has been a general/program/technical chair for eight international conferences. 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 a senior member of IEEE, Chair of the IEEE CIS Evolutionary Computation Technical Committee, a member of IEEE CIS Intelligent System Applications Technical Committee, 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 the IEEE New Zealand Central Section.

Dr Muhammad Iqbal completed his PhD in Learning Classifier Systems (LCS) under the supervision of A/Prof Will Browne and Prof Mengjie Zhang, in the Evolutionary Computation Research Group (ECRG), at the School of Engineering and Computer Science, Victoria University of Wellington (VUW), New Zealand. He is currently working as a postdoctoral research fellow at VUW, New Zealand. Iqbal's main research interests are in the area of evolutionary machine learning. His research focuses on evolutionary image analysis and classification using transfer learning in genetic programming and learning classifier systems techniques. Iqbal is currently working on multiclass texture classification using genetic programming by extracting useful knowledge from simpler problems to solve complex problems of the domain. He is also interested in medical image analysis, data mining, and scalability of evolutionary techniques.

Dr Yi Mei (S'09-M'13) is a Research Fellow at the School of Engineering and Computer Science, Victoria University of Wellington, New Zealand. His research interests include evolutionary computation in scheduling, routing and other combinatorial optimization problems. Dr. Mei has a number of top-notch publications in IEEE Transactions on Evolutionary Computation, IEEE Transactions on Systems, Man, and Cybernetics: Part B and ACM Transactions on Mathematical Software. As the sole investigator, he won the 2nd prize of the Competition at IEEE World Congress on Computational Intelligence 2014: Optimisation of Problems with Multiple Interdependent Components. He was the recipient of 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). He was ranked top 10% of the unsuccessful applications (near-miss) in ARC DECRA rounds 2014 and 2015. Dr. Mei serves as the committee member of IEEE ECTC Task Force on Evolutionary Scheduling and Combinatorial Optimisation and IEEE CIS Task Force on EC for Feature Selection and Construction.

Dr Bing Xue is currently a Lecturer in Evolutionary Computation Research Group, School of Engineering and Computer Science at Victoria University of Wellington. Her research focuses mainly on evolutionary computation, data mining, and machine learning, particularly, classification, regression, transfer learning and domain adaption, feature selection, feature construction, and multi-objective optimisation. She has over 40 papers published in fully referred international journals and conferences. Dr Xue is the Chair of Task force on Evolutionary Feature Selection and Construction in IEEE Computational Intelligence Society (CIS), Program Co-Chair of the 7th International Conference on Soft Computing and Pattern Recognition (2015), Guest Editor of Special Issue on Evolutionary Optimisation, Feature Reduction and Learning, Soft Computing (Journal), Chair of Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition in IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), Chair of Special session Evolutionary Feature Selection and Construction in IEEE Congress on Evolutionary Computation, WCCI 2016/CEC2016 and CEC2015, and in international conference on Simulated Evolution And Learning (SEAL) 2014. She is also a member of Evolutionary Computation Technical Committee in IEEE CIS. Dr Xue is serving as a reviewer for nearly 20 international journals and a program committee member for over 30 international conferences. She is also serving as the Director of Women in Engineering for the IEEE New Zealand Central Section. Dr Xue is a member of IEEE and IEEE Computational Intelligence Society. 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.

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