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 for neural architecture search. Authors are invited to submit their original and unpublished work to this special session.
Please follow the IEEE-CEC 2021 Call for Papers Web Site.
Papers for IEEE CEC 2021 should be submitted electronically through the Congress website at http://cec2021.mini.pw.edu.pl. All submissions 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 23 papers on deep neural networks and evolutionary algorithms in fully-refereed international journals and conferences including nine papers in top journals IEEE Transactions on Evolutionary Computation, IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Cybernetics. 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 an Associate Professor and Program Director of Science in School of Engineering and Computer Science at VUW. She has over 200 papers published in fully refereed international journals and conferences and her research focuses mainly on evolutionary computation, machine learning, classification, symbolic regression, feature selection, evolving deep neural networks, image analysis, transfer learning, multi-objective machine learning.
Dr Xue is currently the Chair of IEEE Computational Intelligence Society (CIS) Data Mining and Big Data Analytics Technical Committee, and Vice-Chair of IEEE Task Force on Evolutionary Feature Selection and Construction, Vice-Chair of IEEE CIS Task Force on Transfer Learning & Transfer Optimization, and of IEEE CIS Task Force on Evolutionary Deep Learning and Applications.
A/Prof Xue is the organiser of the special session on Evolutionary Feature Selection and Construction in IEEE Congress on Evolutionary Computation (CEC) 2015, 2016, 2017, 2018 2019, and 2020. A/Prof Xue has been a chair for a number of international conferences including the Chair of Women@GECCO 2018 and a co-Chair of the Evolutionary Machine Learning Track for GECCO 2019 and 2020. She is the Lead Chair of IEEE Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition (FASLIP) at SSCI 2016, 2017,2018, 2019 and 2020, a Program Co-Chair of the 7th International Conference on Soft Computing and Pattern Recognition (SoCPaR2015), a Program Chair of the 31th Australasian Joint Conference on Artificial Intelligence (AI 2018), and Finance Chair for 2019 IEEE Congress on Evolutionary Computation.
She is an Associate Editor or Member of the Editorial Board for seven international journals, including IEEE Transactions of Evolutionary Computation, IEEE Computational Intelligence Magazine, and ACM Transactions on Evolutionary Learning and Optimisation.
Chuan-Kang Ting received the B.S. degree from National Chiao Tung University, Taiwan, the M.S. degree from National Tsing Hua University, Taiwan, and the Ph.D. degree from the University of Paderborn, Germany. He is currently a Professor at Department of Power Mechanical Engineering, National Tsing Hua University, Taiwan. His research interests include evolutionary computation, computational intelligence, metaheuristic algorithms, and their applications in intelligent systems, machine learning, bioinformatics, networks, music and games.
He is the Editor-in-Chief of IEEE Computational Intelligence Magazine, an Associate Editor of 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.