Introduction:Many of the tasks carried out in data mining and machine learning, such as feature subset selection, associate rule mining, model building, etc., can be transformed as optimization problems. Thus it is very natural that Evolutionary Computation (EC), has been widely applied to these tasks in the fields of data mining (DM) and machine learning (ML), as an optimization technique.
On the other hand, EC is a class of population-based iterative algorithms, which generate abundant data about the search space, problem feature and population information during the optimization process. Therefore, the data mining and machine learning techniques can also be used to analyze these data for improving the performance of EC. A plethora of successful applications have been reported, including the creation of new optimization paradigm such as Estimation of Distribution Algorithm, the adaptation of parameters or operators in an algorithm, mining the external archive for promising search regions, etc.
However, there remain many open issues and opportunities that are continually emerging as intriguing challenges for bridging the gaps between EC and DM. The aim of this special session is to serve as a forum for scientists in this field to exchange the latest advantages in theories, technologies, and practice.
We invite researchers to submit their original and unpublished work related to, but not limited to, the following topics:
- EC Enhanced by Data Mining and Machine Learning Concepts and/or Method
- Data Mining and Machine Learning Based on EC Techniques
- Data Mining and Machine Learning Enhanced Multi-Objective Optimization
- Data Mining and Machine Learning Enhanced Constrained Optimization
- Data Mining and Machine Learning Enhanced Memetic Computation
- Multi-Objective Optimization and Rule Mining Problems
- Knowledge Discovery in Data Mining via Evolutionary Algorithm
- Genetic Programming in Data Mining
- Multi-Agent Data Mining using Evolutionary Computation
- Medical Data Mining with Evolutionary Computation
- Evolutionary Computation in Intelligent Network Management
- Evolutionary Clustering in Noisy Data Sets
- Big Data Projects with Evolutionary Computation
- Real World Applications
Zhun FanDepartment of Electronic Engineering, Shantou University, Shantou, China
Zhun Fan received his Ph.D. (Electrical and Computer Engineering) in 2004 from the Michigan State University. He received the B.S. degree in 1995 and M.S degree in 2000, both from Huazhong University of Science and Technology, China. From 2004 to 2011, he was employed as an Assistant Professor and Associate Professor at the Technical University of Denmark. He has also been working at the BEACON Center for Study of Evolution in Action at Michigan State University. He is currently a Professor and Head of Department of Electronic and Informatics Engineering at the Shantou University, China. He is also the Director of the Guangdong Provincial Key Laboratory of Digital Signal and Image Processing. His major research interests include applying evolutionary computation and computational intelligence in design automation and optimization of mechatronic systems, computational intelligence, wireless communication networks, MEMS, intelligent control and robotic systems, robot vision etc
Xinye CaiNanjing University of Aeronautics and Astronautics, Nanjing, China
Xinye Cai received his BEng. Degree in Electronic&Information Engineering Department from Huazhong Univeristy of Science&Technology, China in 2004, and a Msc. degree in Electronic Department University of York, UK in 2006. Later, he received his PhD degree in Electrical&Computer Engineering Department in Kansas State University in 2009. Currently, he is an Associate Professor with the College of Computer Science and Technology, Nanjing University of Aeronautics&Astronautics, China. His main research interests include evolutionary computation, multi-objective optimization, constrained optimization and relevant real-world application.
Chuan-Kang TingNational Chung Cheng University, Chiayi, Taiwan
Chuan-Kang Ting (S’01–M’06–SM’13) 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 an Associate Professor at the Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan. His research interests are in evolutionary computation, computational intelligence, metaheuristic algorithms, and their applications in computer networks, data mining, bioinformatics, music and games.
Jun ZhangSun Yat-Sen University, Guangzhou, China.
Jun Zhang (M’02–SM’08) received the Ph.D. degree in electrical engineering from the City University of Hong Kong, Kowloon, Hong Kong, in 2002. From 2003 to 2004, he was a Brain Korean 21 Post-Doctoral Fellow with the Department of Electrical Engineering and Computer Science, Korea Advanced Institute of Science and Technology, Daejeon, Korea. Since 2004, he has been with Sun Yat-Sen University, Guangzhou, China, where he is currently a Cheung Kong Professor with the Department of Computer Science. He has authored seven research books and book chapters, and over 100 technical papers in his research areas. His current research interests include computational intelligence, cloud computing, high performance computing, data mining, wireless sensor networks, operations research, and power electronic circuits. Dr. Zhang was a recipient of the China National Funds for Distinguished Young Scientists from the National Natural Science Foundation of China in 2011 and the First-Grade Award in Natural Science Research from the Ministry of Education, China, in 2009. He is currently an Associate Editor of the IEEE Transactions on Evolutionary Computation, the IEEE Transactions on Industrial Electronics, the IEEE Transactions on Cybernetics, and the IEEE Computational Intelligence Magazine. He is the Founding and Current Chair of the IEEE Guangzhou Subsection and IEEE Beijing (Guangzhou) Section Computational Intelligence Society Chapters.
K. C. TanDepartment of Electrical and Computer Engineering, National University of Singapore, Singapore
TAN Kay Chen received the B. Eng degree with First Class Honors in Electronics and Electrical Engineering, and the Ph.D. degree from the University of Glasgow, Scotland, in 1994 and 1997, respectively. He is actively pursuing research in computational and artificial intelligence, with applications to multi-objective optimization, scheduling, automation, data mining, and games. Dr Tan has published over 100 journal papers, over 100 papers in conference proceedings, co-authored 5 books including Multiobjective Evolutionary Algorithms and Applications (Springer-Verlag, 2005), Modern Industrial Automation Software Design (John Wiley, 2006; Chinese Edition, 2008), Evolutionary Robotics: From Algorithms to Implementations (World Scientific, 2006; Review), Neural Networks: Computational Models and Applications (Springer-Verlag, 2007), and Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms (Springer-Verlag, 2009), co-edited 4 books including Recent Advances in Simulated Evolution and Learning (World Scientific, 2004), Evolutionary Scheduling (Springer-Verlag, 2007), Multiobjective Memetic Algorithms (Springer-Verlag, 2009), and Design and Control of Intelligent Robotic Systems (Springer-Verlag, 2009). Dr Tan has been invited to be an invited keynote/plenary speaker for over 30 international conferences. He served in the international program committee for over 100 conferences and involved in the organizing committee for over 40 international conferences, including the General Co-Chair for IEEE Congress on Evolutionary Computation 2007 in Singapore. Dr Tan is the General Co-Chair for IEEE World Congress on Computational Intelligence 2016 in Vancouver, Canada. Dr Tan is an IEEE Distinguished Lecturer of IEEE Computational Intelligence Society since 2011. Dr Tan is currently the Editor-in-Chief of IEEE Computational Intelligence Magazine (CIM). He also serves as an Associate Editor / Editorial Board member of over 20 international journals, such as IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, IEEE Transactions on Computational Intelligence and AI in Games, Evolutionary Computation (MIT Press), European Journal of Operational Research, Journal of Scheduling etc. Dr Tan is the awardee of the 2012 IEEE Computational Intelligence Society (CIS) Outstanding Early Career Award for his contributions to evolutionary computation in multi-objective optimization. He also received the Recognition Award (2008) from the International Network for Engineering Education & Research (iNEER) for his outstanding contributions to engineering education and research.
Qingfu ZhangSchool of Computer Science & Electronic Engineering, University of Essex, Essex, UK
Qingfu Zhang is currently a Professor with the School of Computer Science and Electronic Engineering, University of Essex, UK. His is also a Changjiang Visiting Chair Professor in Xidian University, China. From 1994 to 2000, he was with the National Laboratory of Parallel Processing and Computing, National University of Defence Science and Technology, China, Hong Kong Polytechnic University, Hong Kong, the German National Research Centre for Information Technology (now Fraunhofer-Gesellschaft, Germany), and the University of Manchester Institute of Science and Technology, Manchester, U.K. He holds two patents and is the author of many research publications. His main research interests include evolutionary computation, optimization, neural networks, data analysis, and their applications. Dr. Zhang is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the IEEE Transactions on Systems, Man, and Cybernetics–Part B. He is also an Editorial Board Member of three other international journals. MOEA/D, a multobjevitve optimization algorithm developed in his group, won the Unconstrained Multiobjective Optimization Algorithm Competition at the Congress of Evolutionary Computation 2009, and was awarded the 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award.