02/07 - 05/07, 2023, Chicago, USA
Symbolic Regression and modelling 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 regression and modeling can be achieved by a set of techniques including but not only limited to genetic programming, artificial immune system programming and learning classifier systems.
The development in symbolic regression and modeling is motivated by the need to efficiently and effectively convert the data into actionable knowledge. The key characteristic that distinguishes symbolic regression and 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 regression and 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 regression and modelling, covering ALL different evolutionary computation paradigms. The aim is to investigate both the new theories and methods in symbolic regression and modelling, and their applications. This involves contributions to the state-of-the-art symbolic regression and 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 regression and modelling. Novel applications of symbolic regression and 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
Dr Qi Chen is currently a Lecturer 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 over 40 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.
Prof Bing Xue is currently a Professor of Artificial Intelligence, and Deputy Head of School in the School of Engineering and Computer Science at Victoria University of Wellington, and leading the strategic research direction on evolutionary feature selection and construction, and co-leading the Evolutionary Computation Research Group. Her research focuses mainly on evolutionary computation, feature selection, feature construction, multi-objective optimisation, data mining and machine learning.
She has over 200 papers published in fully referred international journals and conferences and most of them are on evolutionary feature selection and construction. She is the organiser of the special session on Evolutionary Feature Selection and Construction in IEEE Congress on Evolutionary Computation (CEC) 2015, 2016, 2017, 2018 and 2019, and the chair of the special session on Evolutionary Feature Reduction in the international conference on Simulated Evolution And Learning (SEAL) 2014. 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, which attracted over 100 submissions.
Prof Xue is an Associate Editor or Member of the Editorial Board for seven international journals, including IEEE Computational Intelligence Magazine, ACM Transactions on Evolutionary Learning and Optimisation, Applied Soft Computing, International Journal of Swarm Intelligence, and International Journal of Computer Information Systems and Industrial Management Applications. She is serving as a reviewer of over 15 international journals including IEEE Transactions on Evolutionary Computation, IEEE Transaction on Cybernetics, and Information Sciences.
Prof 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 current Chair for the IEEE Data Mining Technical Committee. 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.
Prof Mengjie Zhang is a Fellow of Royal Society of New Zealand, a Fellow of Engineering of New Zealand, a Fellow of IEEE and an IEEE Distinguished Lecturer, 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 CEC 2019 General Co-Chair, WCCI 2006, WCCI 2016, WCCI 2018 Special Session Co-chair, WCCI 2020 Publication Co-chair, CEC 2021 Tutorial Chair. 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. He is also 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.
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.