Museum of New Zealand Te Papa Tongarewa, Wellington, New Zealand, 10-13 June 2019
Symbolic modeling is the process of developing/searching for some symbolic descriptions to capture the structure of the data and make an accurate prediction in the numerical data space. These descriptions are usually represented in mathematical models, which are composed of variables and operators to represent the underlying relationship between the independent/input variables and dependent/target variable(s). In evolutionary computation, symbolic modeling can be achieved by a set of techniques including but not only limited to genetic programming and learning classifier systems.
The development in symbolic modeling is motivated by the need to efficiently and effectively convert the data into actionable knowledge. The key characteristic that distinguishes symbolic modeling techniques from numerical modeling techniques, e.g. neural networks, support vector regression and linear regression, is their capability of producing analysable and interpretable models. The interpretability heightens an insightful understanding of the data generating system and the theory in the field of interest. Apart from the interpretability, symbolic modeling is also driven by a number of other metrics, such as the accuracy, the generalisation capacity, the robustness on noisy data, the number and type of features/variables involved in the models, and the shape/structure of the models. Techniques to handle all these aspects are necessary to be further explored.
The theme of this special session is the use of evolutionary computation for symbolic modelling, covering ALL different evolutionary computation paradigms. The aim is to investigate both the new theories and methods in symbolic modelling, and their applications. This involves contributions to the state-of-the-art symbolic modelling techniques through either theoretical work or algorithmic developments investigating and enhancing the complexity, the learning performance, the generalisation, the interpretability, the efficiency and the robustness of the techniques. This also involves methods to handle complex large-scale datasets and high-dimensional datasets, e.g. instance selection/sampling, feature selection and feature construction for symbolic modelling. Novel applications of symbolic modelling are also an important and interesting topic to explore. Authors are invited to submit their original and unpublished work to this special session.
Topics of interest include but are not limited to:
Qi Chen School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand. Qi.Chen@ecs.vuw.ac.nz Phone: +64-4-463 5233 x 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.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
Qi Chen is currently a Postdoctoral Research Fellow in School of Engineering and Computer Science at Victoria University of Wellington. Her research focuses mainly on data mining, machine learning, evolutionary computation, symbolic regression, feature manipulation. She has 10 papers published in fully referred international journals and conferences and most of them are on symbolic modeling. Dr Chen has been serving as a program committee member of over ten international conferences including AAAI, IEEE CEC, IEEE SSCI, Australian AI and SEAL. She is serving as a reviewer of over ten international journals including IEEE Transactions on Cybernetics and IEEE Transactions on Evolutionary Computation.
Dr Bing Xue is currently a Senior Lecturer in School of Engineering and Computer Science at Victoria University of Wellington. Her research focuses mainly on evolutionary computation, feature selection, feature construction, multi-objective optimisation, data mining and machine learning. She is currently the Chair of the IEEE CIS Task Force on Evolutionary Feature Selection and Construction, Vice Chair of the IEEE CIS Data Mining and Big Data Analytics Technical Committee, and an Associate Editor/member of Editorial Board for five international journals including IEEE Computational Intelligence Magazine, Applied Soft Computing, International Journal of Swarm Intelligence, and International Journal of Computer Information Systems and Industrial Management Applications. She is a Guest Editor for the Special Issue on Evolutionary Feature Reduction and Machine Learning for the Springer Journal of Soft Computing. She is also a Guest Editor for Evolutionary Image Analysis and Pattern Recognition in Journal of Applied Soft Computing. She has been a chair for a number of international conferences including the Leading Chair of IEEE Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition at SSCI 2016, 2017 and 2018, a Program Co-Chair of the 31th Australasian AI 2018, ACALCI 2018, and the 7th International Conference on SoCPaR2015, Special Session Chair for The 20th Asia-Pacific Symposium on Intelligent and Evolutionary Systems (IES2016), a Tutorial Chair for the 30th Australasian AI, and publicity chair for the international conference on Simulated Evolution And Learning (SEAL) 2017. She is the organiser of the special session on Evolutionary Feature Selection and Construction in IEEE Congress on Evolutionary Computation (CEC) 2015, 2016, 2017 and 2018, and SEAL 2014 and 2017. Dr Xue is chairing the IEEE CIS Graduate Student Research Grants Committee and the Secretary of the IEEE Chapter on Computational Intelligence in that Section.
Prof Mengjie Zhang is a Fellow of Royal Society New Zealand, 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, 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), 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 current Chair of IEEE CIS Intelligent Systems Applications, the immediate Past Chair of 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, and the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.