Deep learning has shown promising performance in addressing diverse real-world problems. The achievements of such algorithms are contributed greatly by their deep models. However, designing an optimal model often requires rich domain knowledge on both the investigated data and the general data analysis domain, which is not necessarily held by each interested end-user. In addition, the deep model defined for the task is often not reusable, and a new model must be redesigned for data with a slightly changed scenario. These facts collectively promote the development of techniques for automatically designing the models. In literature, this kind of automation is generally formulated as an optimization problem with non-convex and non-differentiable characteristics, challenging the design of proper optimization algorithms for solving the problem.
Evolutionary computation (EC) approaches, particularly genetic algorithms, particle swarm optimization and genetic programming, have shown superiority in addressing real-world optimization 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. EC approaches have great potential in automatically designing the optimal deep models. Indeed, there have been lots of EC works applied to the automation during past years, such as the architectures of deep neural networks. However, the development is still in the infant, and there still large room of designing novel efficient and effective EC methods to obtain satisfactory models.
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, optimization techniques, and fitness evaluation for automating the design of deep models. Authors are invited to submit their original work to this special session. The submissions of this special session, which receive very positive comments during the review phase, will be invited for substantial extension to submit to IEEE Transactions on Evolutionary Computation special issues on “evolutionary neural architecture search”.
Please follow the IEEE-CEC 2023 Paper submission Web Site.
Papers will be reviewed using a double blind review process. After being reviewed, papers may receive an Accept, Reject, or Revise–and–Resubmit.
The review process for CEC 2023 will be double-blind, i.e. reviewers will not know the authors’ identity (and vice versa). Authors should ensure their anonymity in the submitted papers. In brief:
Yanan Sun is a Professor at 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 Ph.D. 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 Ph.D. student.
Dr. Sun’s research interests focus on evolutionary algorithms for automating neural architecture design. In this topic, he has published >50 papers in fully-refereed international journals and conferences including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, CEC, GECCO, and NeurIPS. He also led the authored book “Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances” published by Springer. He is the founding chair of the IEEE CIS Task Force on “Evolutionary Deep Learning and Applications”. He was selected as “World’s Top 2% Scientists” by Sandford University in both 2021 and 2022.
Bing Xue is currently a Professor of Artificial Intelligence, and Deputy Head of School in the School of Engineering and Computer Science at VUW. She received the B.Sc. degree from the Henan University of Economics and Law, Zhengzhou, China, in 2007, the M.Sc. degree in management from Shenzhen University, Shenzhen, China, in 2010, and the Ph.D. degree in computer science in 2014 at VUW, New Zealand. She has over 300 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 NNs, image analysis, transfer learning, multi-objective machine learning.
Dr Xue is currently the Chair of IEEE CIS Evolutionary Computation Technical Committee, Chair of IEEE CIS Task Force on Evolutionary Deep Learning and Applications, and Editor of IEEE CIS Newsletter. She has also served as associate editor of several international journals, such as IEEE Computational Intelligence Magazine, IEEE Transactions on Evolutionary Computation and ACM Transactions on Evolutionary Learning and Optimization.
Chuan-Kang Ting is currently a Professor and the Chair of Department of Power Mechanical Engineering, National Tsing Hua University. He received the B.S. degree from National Chiao Tung University, Hsinchu, Taiwan, in 1994, the M.S. degree from National Tsing Hua University, Hsinchu, in 1996, and the Dr. rer. nat. degree in computer science from Paderborn University, Paderborn, Germany, in 2005. His research interests include evolutionary computation, computational intelligence, machine learning, and their applications in machinery, manufacturing, ethics, music and arts.
Dr. Ting is the Editor-in-Chief of IEEE Computational Intelligence Magazine and Memetic Computing, an Associate Editor of IEEE Transactions on Emerging Topics in Computational Intelligence, and an Editorial Board Member of Soft Computing. He served as the IEEE Computational Intelligence Society (CIS) Newsletter Editor, the IEEE CIS Webmaster, the Chair of IEEE CIS Chapters Committee, and the Chair of IEEE CIS Creative Intelligence Task Force. He is an Executive Board Member of Taiwanese Association for Artificial Intelligence.
Mengjie Zhang is currently a Professor of Computer Science, Head of the Evolutionary Computation Research Group, and the Associate Dean (Research and Innovation) in the Faculty of Engineering, VUW. He received the B.E. and M.E. degrees from Artificial Intelligence Research Center, Agricultural University of Hebei, Hebei, China, and the Ph.D. degree in computer science from RMIT University, Melbourne, VIC, Australia, in 1989, 1992, and 2000, respectively. His current research interests include evolutionary computation, particularly genetic programming and particle swarm optimization with application areas of image analysis, multi-objective optimization, feature selection and reduction, job shop scheduling, and transfer learning. He has published over 700 papers in refereed international journals and conferences.
Prof. Zhang is a Fellow of the Royal Society of New Zealand, a Fellow of IEEE, an IEEE CIS Distinguished Lecturer, and have been a Panel Member of the Marsden Fund (New Zealand Government Funding). He was the chair of the IEEE CIS Intelligent Systems and Applications Technical Committee, the chair of the IEEE CIS Emergent Technologies Technical Committee and the Evolutionary Computation Technical Committee, and a member of the IEEE CIS Award Committee.