Deep learning has shown significantly promising performance in addressing real-world problems, such as image recognition, natural language processing and self-driving. The achievements of such algorithms owe to its deep structures. However, designing an optimal deep structure for a particular problem requires rich domain knowledge on both the investigated data and the general data analysis domain, which is not necessarily held by the end-users. In addition, the problem of searching for the optimal structure could be non-convex and non-differentiable, and existing accurate methods are incapable of well addressing it. Furthermore, the deep structure defined for the task is not reusable, i.e., a new one must be redesigned for data with a slightly changed scenario and/or unseen data.
Evolutionary computation (EC) approaches, particularly genetic algorithms (GAs), particle swarm optimization (PSO) and genetic programming (GP), have shown superiority in addressing real-world problems due largely to their powerful abilities in searching for global optima, dealing with non-convex/non-differentiable problems, and requiring no rich domain knowledge. However, most of the existing EC methods currently work only on relatively shallow structures, and cannot provide satisfactory results in searching for deep structures. In this regard, deep learning structures designed by EC approaches, i.e., evolutionary deep learning, would be a great research topic.
The theme of this special session aims to bring together researchers investigating methods and applications in evolutionary deep learning. Particularly, the methods focus on effective and efficient representations, search mechanisms and optimization techniques. Authors are invited to submit their original and unpublished work to this special session.
Please follow the IEEE WCCI 2020 Call for Papers Web Site. Papers for IEEE WCCI 2020 should be submitted electronically using the Congress website www.wcci2020.org and will be refereed by experts in the fields and ranked based on the criteria of originality, significance, quality and clarity.
Yanan Sun is a Professor (research) in the College of Computer Science at Sichuan University, China. Before that, he was a Postdoctoral Research Fellow in the School of Engineering and Computer Science at Victoria University of Wellington in New Zealand from July 2017 to March 2019. He received his PhD degree from the College of Computer Science at Sichuan University in China in June 2017. From August 2015 to February 2017, he studied in the School of Electrical and Computer Engineering at Oklahoma State University in the USA, as a joint PhD student financed by China Scholar Council. Dr Sun has published 20 papers on deep neural networks and evolutionary algorithms in fully-refereed international journals and conferences including four papers in top journals IEEE Transactions on Evolutionary Computation, IEEE Transactions on Neural Networks and Learning Systems and Knowledge-based System. Although being emergent, he has been a reviewer of >30 international journals/conferences and program committee member for international conferences. Further, the paper “Evolving Unsupervised Deep Neural Networks for Learning Meaningful Representations”, where Dr sun is the first author, is the first one published by IEEE Transactions on Evolutionary Computation on the topic of evolutionary deep learning. He is co-organizer of the first workshop on “Evolutionary Deep Learning”, the founding chair of IEEE CIS Task Force on “Evolutionary Deep Learning and Applications” and committee member of IEEE CIS Graduate Student Research Grants.
Bing Xue is currently a Associate Professor 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 has over 100 papers published in fully referred international journals and conferences. She is currently co-supervising over 10 PhD students and visiting scholars, and over 10 Honours and summer research projects. Dr Xue is currently the Chair of the IEEE CIS Task Force on Evolutionary Feature Selection and Construction, 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 is serving as a reviewer of over 20 international journals including IEEE Transactions on Cybernetics and IEEE Transactions on Evolutionary Computation. 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 and 2017, 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, publicity chair for the international conference on Simulated Evolution And Learning (SEAL) 2017, and International Conference on Data, Intelligence and Security (ICDIS) 2018. She is the organiser of the special session on Evolutionary Feature Selection and Construction in IEEE Congress on Evolutionary Computation (CEC) 2015, 2016 and 2017, and SEAL 2014 and 2017.
Chuan-Kang Ting received the B.S. degree from National Chiao Tung University, Taiwan, in 1994, the M.S. degree from National Tsing Hua University, Taiwan, in 1996, and the Ph.D. degree from the University of Paderborn, Germany, in 2005. He is currently a Professor at Department of Power Mechanical Engineering, National Tsing Hua University, Taiwan. His research interests are in evolutionary computation, computational intelligence, metaheuristic algorithms, and their applications in transportation and logistics networks, bioinformatics, music and games. He is an Associate Editor of IEEE Computational Intelligence Magazine and IEEE Transactions on Emerging Topics in Computational Intelligence, and an Editorial Board Member of Soft Computing and Memetic Computing journals. He chaired the AI Forum 2012 and co-chaired the 2013 IEEE Symposium on Computational Intelligence for Creativity and Affective Computing.
Mengjie Zhang is a Fellow of Royal Society of NZ, a Fellow of IEEE, and currently Professor of Computer Science (Artificial Intelligence) 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, feature selection/construction and dimensionality reduction, computer vision and image processing, job shop scheduling, automated deep learning and transfer learning, and classification with unbalanced and missing data. 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 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 was the Chair of the IEEE CIS Intelligent Systems and Applications Technical Committee (ISATC), a member of the IEEE CIS Evolutionary Computation Technical Committee, a Vice-Chair of the IEEE CIS Task Force on Evolutionary Computer Vision and Image Processing, a Vice-Chair of the IEEE CIS Task Force on Evolutionary Computation for Feature Selection and Construction, a member of IEEE CIS Task Force of Hyper-heuristics, and the Founding Chair for IEEE Computational Intelligence Chapter in New Zealand.