With the over-flooding of big data, researchers and practitioners have started showing remarkable interest to explore the data space, and have considered that structuralized knowledge reasoning is an effective computational paradigm for dealing with big data tasks. Granular computing (GC) focuses on the knowledge representation and reasoning with information granules, and fuzzy sets and rough sets are two crucial branches of GC. Fuzzy sets theory was introduced to represent concepts with ambiguous boundaries and to understand the processes of complex human reasoning. It has become a popular tool for the design of fuzzy classifiers. Rough sets theory was presented to quantitatively analyze the uncertainty and to process incomplete knowledge. It can find a decision-making table between the strict statistics and random distribution. Since rough set theory can typically describe the uncertainty of knowledge, it has been extensively used in data mining, knowledge discovery, and intelligent system. It is a promising line of work for the design of efficient granular computing model and method for handling big data.
The global search performed by evolutionary computation algorithms frequently provides a valuable complement to the local search of non-evolutionary methods, and combinations of granular computing and evolutionary computation often show particular promise in practice. Evolutionary computation for granular computing emphasizes the utility of different evolutionary algorithms to various facets of granular computing, ranging from theoretical analysis to real-life applications. The main motivation for applying evolutionary algorithms to granular computing tasks in the knowledge reasoning is that they are robust and adaptive search methods, which can perform a global search in the space of candidate solutions. It has been a hot trend to address the classical and new-emerging granular computing problems by using different evolutionary algorithms. The benefits of exploring the combination of granular computing and evolutionary computation in the knowledge reasoning scenario will have an impact in multiple research disciplines and industry domains, including transportation, communications, social network, medical health, and so on.
The goal of this special section aims at providing a specific opportunity to review the state-of-the-art of evolutionary computation for granular computing, and bringing together researchers in the relevant areas to discuss the latest progress, new research methodologies and potential research topics. The selected papers will be beneficial to both academia and industry, for delivering the latest research results and inspiring new directions to study.
The topics of interest include, but are not limited to:
Fuzzy sets method and system with evolutionary algorithm
Rough sets method and system with evolutionary algorithm
Probabilistic granules model with evolutionary algorithm
Shadowed sets model with evolutionary algorithm
Multi-objective evolutionary algorithm for granular computing
Evolutionary fuzzy deep neural network for data classification
Granular computing framework for big data analytic by evolutionary algorithm
Evolutionary multimodal optimization for fuzzy rough system
Evolutionary multimodal optimization for rough fuzzy system
Quantum-inspired evolutionary algorithm for granular computing
Co-evolutionary algorithm for granular computing framework
Adaptive granular computing framework with evolutionary algorithm
Convergence analysis of evolutionary algorithm for granular computing
Evolutionary optimization with dynamic parameter adaptation for fuzzy system
Granular data mining for feature learning, classification, regression, and clustering with evolutionary algorithm
Granular data mining for multi-task modeling, multi-view modeling and co-learning with evolutionary algorithm
Real-world applications using evolutionary granular computing methods
Papers should be submitted following the instructions at the IEEE CEC 2019 web site. Please select the main research topic as the Special Session on “Evolutionary Computation for Granular Computing”. All papers accepted and presented at CEC2019 will be included in the conference proceedings published by IEEE Explore, which are typically indexed by EI. Information on the format and templates for papers can be found here:http://www.cec2019.org/papers.html#templates.
Paper submission: 7 January, 2019
Decision notification: 7 March, 2019
Camera ready paper due: 31 March, 2019
Registration: 31 March, 2019
Conference: 10 June, 2019
Nantong University, China.
Email address: email@example.com
Gary G. Yen
Oklahoma State University, U.S.A.
Email address: firstname.lastname@example.org
Session Organizer 1
Weiping Ding (M’16) received the Ph.D. degree in Computation Application, Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2013. He is an Associate Professor, Deputy Head of Computer Science and Technology Department, Nantong University, China. His research interests include fuzzy sets, rough sets, evolutionary computation, data mining, machine learning, and big data analytics. He was a Visiting Researcher at University of Lethbridge, Alberta, Canada, in 2011. From 2014 to 2015, He is a Postdoctoral Researcher at the Brain Research Center, National Chiao Tung University (NCTU), Hsinchu, Taiwan, China. In 2016, He was a Visiting Scholar at National University of Singapore (NUS), Singapore. From 2017 to 2018, he was a Visiting Scholar at University of Technology Sydney (UTS), Australia. He is a member of IEEE, ACM and Senior CCF.
Dr. Ding has published over 60 papers in top journals and prestigious conferences as the first author, including IEEE Transactions on Fuzzy Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Information Sciences, Expert System with Application, Knowledge-Based Systems, Neurocomputing and so on. To data, he has held 10 approved invention patents in total over 20 issued patents. Dr. Ding was a recipient of Computer Education Excellent Paper Award (First-Prize) from the National Computer Education Committee of China, in 2009. He was an Excellent-Young Teacher (Qing Lan Project) of Jiangsu Province in 2014, and a High-Level Talent (Six Talent Peak) of Jiangsu Province in 2016. He was awarded the Best Paper of ICDMA’15, and an Outstanding Teacher of Software Design and Entrepreneurship Competition by the Ministry of Industry and Information Technology, China, in 2017. Dr. Ding was a recipient of the Medical Science and Technology Award (Second-Prize) in 2017, and Jiangsu Provincial Education Teaching and Research Achievement Award (Third-Prize). Dr. Ding was awarded two Chinese Government Scholarships for Overseas Studies in 2011 and 2016.
Dr. Ding served /serves as an Associate Editor of IEEE Transactions on Fuzzy Systems, Information Sciences, Swarm and Evolutionary Computation, and IEEE Access. He severs as a Reviewer in Top-tier Journals such as IEEE Transactions on Fuzzy Systems, IEEE Transactions on Neural Networks and Learning System, IEEE Transactions on Cybernetics, IEEE Transactions on Knowledge and Data Engineering, Information Sciences and so on. He has been serving as a steering committee member and a program committee member for a number of international conferences.
Session Organizer 2
Gary G. Yen received his PhD degree in electrical and computer engineering from the University of Notre Dame in 1992. He worked at the Structural Control Division of the USAF Research Laboratory in Albuqurque during 1992-1996. He is currently a Professor in the School of Electrical and Computer Engineering, Oklahoma State University in Stillwater. His research is supported by the DoD, DoE, EPA, NASA, NSF, and Process Industry. His research interest includes intelligent control, computational intelligence, conditional health monitoring, signal processing and their industrial/defense applications.
Dr. Yen was an associate editor of the IEEE Control Systems Magazine, IEEE Transactions on Control Systems Technology, Automatica, Mechantronics, IEEE Transactions on Systems, Man and Cybernetics, Part A and Part B, IEEE Transactions on Neural Networks, and among others. He is currently serving as an associate editor for the IEEE Transactions on Evolutionary Computation and International Journal of Swarm Intelligence Research. He served as the General Chair for the 2003 IEEE International Symposium on Intelligent Control held in Houston and 2006 IEEE World Congress on Computational Intelligence held in Vancouver. Dr. Yen served as Vice President for the Technical Activities in 2005-2006 and President in 2010-2011 of the IEEE Computational Intelligence Society. He is the founding Editor-in-Chief of the IEEE Computational Intelligence Magazine.
He received KC Wong Fellowship from the Chinese Acadamy of Sciences, Halliburton Outstanding Faculty award, and OSU Regents Distinguished Research award. He also received an Honorary Professorship from Northeastern University, Sichuan University, and Dalian University of Technology in China. In 2011, he received the Andrew P Sage Best Transactions Paper award from the IEEE Systems, Man and Cybernetics Society. In 2013, he received Meritorious Service award from the IEEE Computational Intelligence Society. He is a distinguished lecturer from the IEEE Computational Intelligence Society, 2012-2014, an IEEE Fellow, and IET Fellow.
Hamido Fujita, Iwate Prefectural University, Japan
Shusaku Tsumoto, Shimane University, Japan
Isaac Triguero, University of Nottingham, United Kingdom
Alfredo Petrosino , University of Naples Parthenope, Italy
Bing Xu, Victoria University of Wellington, New Zealand
Shirui Pan, University of Technology Sydney, Australia
Wei Wang, Beijing Jiaotong University, China
José Antonio Iglesias, Carlos III University, Spain
Khan Muhammad , Sejong University, Republic of Korea
Mukesh Prasad , University of Technology Sydney, Australia
Victor Hugo C. de Albuquerque, University of Fortaleza, Brazil
Fuyuan Xiao, Southwest University, China
Xiaowei Gu , Lancaster University, UK
Guoqing Cao, Singapore Management University, Singapore