• Special Session on "When Evolutionary Computation Meets Data Mining"

Introduction:

Many of the tasks carried out in data mining and machine learning,such as feature subset selection,associate   rule mining, and madel building, can be tranformed as optimization problem. Thus it is very natural that          Evolutionary Compution(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 use to analyze these data for             improving the performance of EC. A lot 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, and so on.

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We invite researchers to submit their original and unpublished work related to, but not limited to, the             following topics: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 Framework
• Data Mining and Machine Learning Based on EC techniques
• Machine Learning Enhanced and/or Model-based Multi- and/or Many-objective Optimization
• Data Mining and Machine Learning Enhanced Constrained Optimization:
• Data Mining and Machine Learning Enhance Memetic Computation or Local Search
• Data Mining and Machine Learning Enhance EC for Combinatorial Optimization
• Data Mining and Machine Learning Enhance EC for large-scale Optimization
• Data Mining and Machine Learning Enhance EC for Dynamic Optimization
• Association Rule Mining Based on Multi-Objective Optimization
• 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
• Deep Learning with Evolutionary Computation
• Real World Applications

Paper Submission:

All papers should be submitted electronically through IEEE CEC 2017 website at http://www.cec2017.org/To   submit your papers to the specail session, please select the Session  in the Main Research topic. For more       submission information please vist:http:// www.cec2017.org/ All accepted papers will be published in the IEEE CEC 2017  electronic proceedings.

Organizers:

Zhun Fan

Shantou University, China

E-mail:zfan@stu.edu.cn

Xinye Cai

Nanjing University of Aeronautics and Astronautics, Chian

E-mail:xinye@nuaa.edu.cn

Chuan-Kang Ting

National Chung Cheng University, Taiwan

E-mail:ckting@cs.ccu.edu.tw

Program Committee(Provisional):

• Aimin Zhou, East Chian Normal University, China
• Bin Li, University of Science and Technology of China, China
• Ke Tang, University of Science and Technology of China, China
• Hailin Liu, Guangdong University of Technology, China
• Hui Li, Xi'an Jiaotong University, China
• Wei-Neng Chen, Sun Yat-Sen University, China
• Xiao-Min Hu, Sun Yat-Sen University, China
• Zhi-Hui Zhan, Sun Yat-Een University, China
• Yue-Jiao Gong, Sun Yat-Sen University, China
• Ying-Lin, Sun Yat-Sen University, China
• Jing Liang, Zhengzhou University, China
• Kai Qin, RMIT University, Australia
• Xiaodong Li,RMIT University, Australia
• Kay Chen Tan, National University of Singapore, Singapore
• Licheng Jiao, Xiaodian University, China
• Maoguo Gong, Xidian University, China
• Jing Liu, Xidian university, China
• Dunwei Gong, China University of Ming and Technology, China
• Xiaoyan Sun, China University of Mining and Technology, China
• Ling Wang, Tsinghua University, China
• Mengjie Zhang, Victorria University of Wellington, New Zealand
• Yanfei Zhong, Wuhan University, China
• Yanqing Zhang, Georgia State University, USA
• Yaochu Jin, University of Surrey, UK
• Ying-Ping Chen, National Chiao Tung University, Taiwan
• Yong Wang, Central South University of China, China
• Zhen Ji, Shenzhen University, China
• Erik Goodman, Michigan State University, USA
• Gary Yen, Oklahoma State University, USA
• Weihua Sheng, Oklahoma State University, USA
• Jinchao Liu, VisionMetric, UK
• Stephen L.Smith, University of York, UK
• Sofiane Achiche, Ecole Polytechnique de Montreal, Canada
• Ilmar Santos, Technical University of Denmark, Denmark
• Hui Cheng, Liverpool Jhon Moores University, UK
• Shengxiang Yang, De Montfort University, UK
• Ong Yew Soon, Nanyang Technological University
• Zexuan Zhu, Shenzhen University
• Pan Wang, Wuhan University of Technology, China
• Jinglei Guo, Central China Normal University, China