Tuesday 27 October 2015

Call for papers WCCI 2016 Special Session "Recent trends in many-valued logic and fuzziness"

Many-valued logics have constituted for several decades key conceptual tools for the formal description and management of fuzzy, vague and uncertain information. In the last few years, the study of these logical systems has seen a bloom of new research related to the most diverse areas of mathematics and applied sciences. Relevant recent developments in this field are connected to the natural semantics of non-classical events. A non-classical event is described by a formula in the language of a given many-valued logic. A satisfying semantics for such events must account for their different aspects, in particular the "ontic" aspect, related to their vague nature, and the "epistemic" aspect, related to our ignorance, or approximate knowledge about them. The combination in a unique conceptual framework of the logic and the probability of a class of non-classical events, usually reached through the algebraic semantics and their topological or combinatorial dualities, provides both the theoreticians and the application-oriented scholars with powerful tools to deal with this kind of events.

This special session is devoted to the most recent development in the realm of many-valued logics, with particular emphasis on theoretical advances related to algebraic or alternative semantics, combinatorial aspects, topological and categorical methods, proof theory and game theory, many-valued computation. In particular, results directed towards a better understanding of the natural semantics of non-classical events will be appreciated. Further, a special attention is also given to connections and synergies between many-valued logics and other different formal approaches to vague and approximate reasoning, such as Rough Sets, Formal Concept Analysis and Relational Methods.

A partial list of topics is the following:
  • Algebraic semantics of many-valued logics
  • Applications of many-valued logics to Formal Concept Analysis and Relational Methods
  • Applications of many-valued logics to Fuzzy Sets and to Rough Sets
  • Combinatorial or topological dualities
  • Computational complexity of many-valued logics
  • Many-valued computational models
  • Modal logic approaches to probability and uncertainty in many-valued logics
  • Natural and alternative semantics for many-valued logics
  • Proof theory for many-valued logics
  • Representation theory
  • Subjective probability approaches to many-valued logics and non-classical events

Important Dates

Paper Submission Deadline: 15 January 2016;
Paper Acceptance Notification Date: 15 March 2016;
Final Paper Submission and Early Registration Deadline: 15 April 2016;
IEEE WCCI 2016: 25-29 July 2016.

Submission

Paper submission deadline is on January 15, 2016.
All papers must be submitted through the Online Submission System of the conference.
Please submit your paper selecting the option "Main research topic": Special Session on Recent trends in many-valued logic and fuzziness.

In order for your papers to be included in the congress program and the proceedings, final accepted papers must be submitted and the corresponding registration fees must be paid by April 15, 2016.

Organizers

Stefano Aguzzoli, Università degli Studi di Milano
Pietro Codara, Università degli Studi di Milano
Diego Valota, IIIA-CSIC

For more informations, please visit:
http://www.iiia.csic.es/~diego/fuzzieee2016.htm

Thursday 22 October 2015

Call for Papers WCCI 2016 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, 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   

Paper Submission:

All papers should be submitted electronically through http://sites.ieee.org/cec2016/. To submit your papers to the special session, please select the Special Session name in the Main Research topic. For more submission information, please visit http://sites.ieee.org/cec2016/accepted-ss/. All accepted papers will be published in the IEEE CEC 2016 proceedings, included in the IEEE Xplore digital library.

Co-Organizers

Zhun Fan
Department of Electronic Engineering, Shantou University, Shantou, China
E-mail:  zfan@stu.edu.cn

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 Cai
Nanjing University of Aeronautics and Astronautics, Nanjing, China
E-Mail: xinye@nauu.edu.cn

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 Ting
National Chung Cheng University, Chiayi, Taiwan
E-Mail: ckting@cs.ccu.edu.tw

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 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 music, creativity, data mining, computer networks, and bioinformatics.

Qingfu Zhang
School of Computer Science & Electronic Engineering, University of Essex, Essex, UK
E-Mail: qzhang@essex.ac.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.

(Provisional) Program Committee

  • Aimin Zhou, East China 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-Sen University, China
  • Yue-Jiao Gong, Sun Yat-Sen University, China
  • Ying-Lin, Sun Yat-Sen University, China
  • Jing Liang, Zhenzhou University, China 
  • Kai Qin, School of Computer Science and Information Technology, RMIT University, Australia
  • Xiaodong Li, School of Computer Science and Information Technology, RMIT University, Australia
  • Kay Chen Tan, National University of Singapore, Singapore 
  • Licheng Jiao, Xidian University, China
  • Maoguo Gong, Xidian University, China
  • Jing Liu, Xidian University, China
  • Dunwei Gong,  China University of Mining and Technology, China
  • Xiaoyan Sun,  China University of Mining and Technology, China
  • Ling Wang, Tsinghua University, China
  • Mengjie Zhang, Victoria 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 Montréal, Canada
  • Ilmar Santos, Technical University of Denmark, Denmark
  • Hui Cheng, Liverpool John Moores University, UK. hui.cheng@beds.ac.uk
  • Shengxiang Yang, De Montfort University, UK. syang@dmu.ac.uk
  • Ong Yew Soon, Nanyang Technological University
  • Zexuan Zhu, Shenzhen University

Call for Papers IEEE CIBCB 2016

IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology
The Empress Chiang Mai Hotel
Chiang Mai, Thailand
5-7 October 2016
http://www.cibcb2016.org

Call for Contributed Papers

The annual IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB 2016) is a premier event in the areas of Computational Intelligence in Bioinformatics and Computational Biology. It will be held during the 5th to 7th October 2016 in Chiang Mai, Thailand. This conference will focus on all topics in Computational Intelligence in Bioinformatics and Computational Biology, including, but not limited to:
  • Computational Intelligence in Bioinformatics including
  • Gene expression array analysis
  • Structure prediction and folding
  • Molecular sequence alignment and analysis
  • Metabolic pathway analysis
  • MicroRNA analysis
  • Molecular evolution and phylogenetics
  • Pattern recognition in bioinformatics
  • Real-world applications
  • Computational Intelligence in Computational Biology including
  • Systems and synthetic biology
  • Modeling, simulation, and optimization of biological systems
  • Biological network reconstruction
  • Pattern recognition in computational biology
  • Real-world applications
  • Computational Intelligence in Biomedical Engineering including
  • Medical imaging and pattern recognition
  • Signal and image processing in biomedical engineering
  • Biomedical data modeling and mining
  • Biomedical model parameterization
  • Big data analysis and tools for biological and medical data
  • Biomarker discovery and development
  • Neuroscience
  • Real-world applications

Submissions for oral and poster presentation are invited from researchers, practitioners and students worldwide. Prospective  authors  are invited  to  submit  papers  in  IEEE  format,  including  results,  figures,  and references. Accepted papers longer than six pages will have an additional per page charge.

Call for Special Sessions/Tutorials

Proposals for special sessions/tutorials within the technical scope of the conference are also encouraged. Special Sessions/tutorials  have become a traditional and important part of IEEE CIBCB. Papers submitted for special sessions are to be peer-reviewed with the same criteria used for the contributed papers. Special session/tutorial proposal should include the session title, a brief description of the scope, contact information and brief CV of the organizers/tutorial presenter(s).

For general inquiry, please contact General chair Sansanee Auephanwiriyakul at cibcb2016@gmail.com.
For program inquiry, please contact Program co-chair Nipon Theera-Umpon at nipon@ieee.org.

Important Due Date

Special sessions/tutorials            30 January 2016
Acceptance of special sessions   15 February 2016
Paper submission                        30 April 2016
Notification of acceptance             30 June 2016
Camera ready copy due               30 July 2016

Honorary Chairs

James M. Keller
University of Missouri-Columbia
Gary B. Fogel
Natural Selection, Inc.
 

General Chair

Sansanee Auephanwiriyakul
Chiang Mai University
 

Program Co-Chairs

Nipon Theera-Umpon
Chiang Mai University
Mihail Popescu
University of Missouri-Columbia
 

Finance Chair

Sermsak Uatrongjit
Chiang Mai University
 

Web Development Chair

Sakgasit Ramingwong
Chiang Mai University

Publication Co-Chairs

Dome Potikanond
Chiang Mai University
Patiwet Wuttisarnwattana 
Chiang Mai University

Monday 19 October 2015

Call for Papers WCCI 2016 Special Session: Machine Learning for Computer Vision (MLCV 2016)

Overview:

There is a great interest of machine learning algorithms among the computer vision researchers. Many machine learning algorithms have successfully demonstrated the capability of solving real world problems in the computer vision field. The purpose of the special session on Machine Learning for Computer Vision is to address the latest developments of machine learning algorithms for numerous applications in computer vision.

This Special Session aims to bring together machine learning and computer vision researchers to demonstrate latest progress, emphasize new research questions and collaborate for promising future research direction.

Topics:

The theme of the session is the application of machine learning to computer vision. The list of topics includes and is not restricted to the following:
  • Neural Learning
  • Deep Learning
  • Feature Extraction, Selection and Learning
  • Metric Learning
  • Representation Learning
  • Probabilistic Graphical Models
  • Manifold Learning
  • Hybrid Learning
  • Supervised Learning
  • Unsupervised Learning
  • Support Vector Machines
  • Statistical Learning and Reinforcement Learning for Image Processing
  • Document Processing and Analysis
  • Pattern Recognition Algorithms
  • Audio/Video Processing and Analysis
  • Medical Imaging
  • Object Recognition
  • Face Recognition and Image Analysis
  • Real World Intelligent Computer Vision Applications

Organising Chairs:

Professor Brijesh Verma
School of Engineering and Technology
Central Queensland University
Brisbane, Australia
Email: b.verma@cqu.edu.au

Professor Mohammed Bennamoun
School of Computer Science and
Software Engineering University of Western Australia
Perth, Australia
Email: mohammed.bennamoun@uwa.edu.au

Submission Guidelines:

Paper should be submitted via IEEE WCCI 2016 paper submission system. Please select this session during the submission. Please refer to the instructions from WCCI 2016 website.

Important Dates:

Please refer to the WCCI 2016 website.

Short Biographies:

Brijesh Verma is a Professor and the Director of the Centre for Intelligent Systems (CIS) at Central Queensland University (CQU), Australia. He joined Central Queensland University in August 2003 as an Associate Professor and was promoted to Professor in 2008. Prior to joining Central Queensland University, he was a Senior Lecturer (2001-2003) and a Lecturer (1996-2000) at Griffith University, Australia. His main research interests include Computational Intelligence and Pattern Recognition. He has published 13 books/edited books, 9 book chapters and over 150 papers in journals and conference proceedings. He has served on the organizing and program committees of over fifty national and international conferences including IEEE WCCI 2012 and IEEE IJCNN 2015. He was the Special Sessions Chair at IEEE World Congress on Computational Intelligence 2012 in Brisbane, Australia. He is serving on the editorial boards of six international journals including Editor-in-Chief of International Journal of Computational Intelligence and Applications (IJCIA). He is a Senior Member of IEEE. He was the Chair of IEEE Computational Intelligence Society's Queensland Chapter (2007-2008) and under his leadership the Chapter won IEEE CIS 2009 Outstanding Chapter Award.

Mohammed Bennamoun is a Winthrop Professor at the University of Western Australia (UWA), Australia. He received his M.Sc. from Queen's University, Kingston, Canada in the area of Control Theory, and his PhD from Queen's /QUT in Brisbane, Australia in the area of Computer Vision. He lectured Robotics at Queen's, and then joined QUT in 1993 as an Associate Lecturer. He then became a Lecturer in 1996 and a Senior Lecturer in 1998 at QUT. He was also the Director of a research Centre from 1998-2002. In Jan. 2003, he joined the Department of Computer Science and Software Engineering at The University of Western Australia (UWA) as an Associate Professor and was promoted to Professor in 2007. He served as the Head of the School of Computer Science and Software Engineering at UWA for five years (February 2007-March 2012). He was an Erasmus Mundus Scholar and Visiting Professor in 2006 at the University of Edinburgh. He was also Visiting Professor at CNRS (Centre National de la Recherche Scientifique) and Telecom Lille1, France in 2009, the Helsinki University of Technology in 2006, and the University of Bourgogne and Paris 13 in France in 2002-2003. He is the co-author of the book "Object Recognition: Fundamentals and Case Studies", Springer-Verlag, 2001. He won the "Best Supervisor of the Year" Award at QUT. He also received an award for research supervision at UWA in 2008. He published over 200 journal and conference papers. He served as a guest editor for a couple of special issues in International journals, such as the International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI). He was selected to give conference tutorials at the European Conference on Computer Vision (ECCV) and the International Conference on Acoustics Speech and Signal Processing (IEEE ICASSP). He organized several special sessions for conferences; including a special session for the IEEE International Conference in Image Processing (IEEE ICIP). He was on the program committee of many conferences e.g. 3D Digital Imaging and Modeling (3DIM) and the International Conference on Computer Vision. He also contributed in the organisation of many local and international conferences. His areas of interest include control theory, robotics, obstacle avoidance, object recognition, artificial neural networks, signal/image processing and computer vision.

Wednesday 14 October 2015

WCCI 2016 Tutorials

IEEE WCCI 2016 is pleased to announce the following Tutorials:

  1. Big Data: Technologies and Computational Intelligence Approaches - By Isaac Triguero and Francisco Herrera
  2. Fuzzy Logic and Machine Learning: A Tutorial - By Hamid Tizhoosh
  3. Type-2 Fuzzy Ontology and Fuzzy Markup Language for Real-World Applications - By Chang-Shing Lee
  4. A Sum-of-Squares Framework for Fuzzy Systems Modeling and Control: Beyond Linear Matrix Inequalities - By Kazuo Tanaka
  5. Tutorial on Type-2 Fuzzy Sets and Systems - By Christian Wagner, Jon Garibaldi and Robert John

More Tutorials will be announced soon. Visit http://goo.gl/65IQmF for more information.

Tuesday 13 October 2015

Call for Papers WCCI-IJCNN2016 Special Session on "Mind, Brain, and Cognitive Algorithms"

Organizers: Leonid Perlovsky (Harvard University and Northeastern University, MA; lperl@rcn.com), José F. Fontanari, Asim Roy, Angelo Cangelosi, Daniel Levine.
Recent progress opens new directions for modeling the mind and brain and developing cognitive algorithms for engineering applications. Cognitive algorithms solve traditional engineering problems much better than before, and new areas of engineering are opened modeling human abilities in cognition, emotion, language, art, music, cultures. Cognitive dissonances and behavioral economics is another new active area of research. A wealth of data are available about the ways humans perform various cognitive tasks (e.g., the knowledge instinct, scene and object recognition, language acquisition, interaction of cognition and language, aesthetic emotions, music cognition, cognitive dissonance) as well as about the biases involved in human judgment and decision making (e.g., the prospect theory and the fuzzy-trace theory). A wealth of data on the web can be exploited for extracting cognitive data. Explaining these laws and biases using realistic neural networks architectures, including neural modeling fields, as well as more traditional learning algorithms requires a multidisciplinary effort.

The aim of this special session is to provide a forum for the presentation of the latest data, results, and future research directions on the mathematical modeling of higher cognitive functions using neural networks, neural modeling fields, as well as cognitive algorithms exploiting web data and solving traditional and new emerging engineering problems, including genetic association studies, medical applications, Deep Learning, and Big Data.

The special session invites submissions in any of the following areas:
  • Neural network models of higher cognitive function
  • Neural mechanisms of emotions, cognition
  • Embodied cognition modeling
  • Neural modeling fields (NMF) 
  • Perceptual processing
  • Language learning 
  • Cognitive and emotional processing
  • Cognitive models of decision-making
  • Models of emotional mechanisms
  • Models of cognitive dissonances
  • Cognitive, language, and emotional models of cultures
  • Cognitive functions of art, music, and spiritual emotions.
  • Emotions in cognition (affective cognition)
  • Aesthetic emotions
  • Cognitive dissonance, neural models
  • Cognition and cultures
  • Medical applications 
  • Genome association studies
  • Big Data

Keywords:

Cognition, Emotions, Decision-Making, Dynamic Logic, Language Acquisition, Language Emotionality, Cognitive Dissonance, Music Cognition, Models of Cultures, Neural Modeling Fields, ART Neural Network, Fuzzy-Trace Theory, Prospect Theory, Deep Learning, Genome Associations, Big Data   

Program Committee:

M. Cabanac (Canada)
A. Cangelosi (UK)
J. F. Fontanari (Brazil)
Y. R. Fu (USA)
R. Illin (USA)
B. Kovalerchuk  (USA)
R. Kozma (USA)
D. Levine (USA)
D. Marocco  (UK)
A. Minai (USA)
L. I. Perlovsky (USA)
S. Petrov (USA)
A. Roy (USA)
F. Schoeller (France)
J. Weng (USA)

WCCI 2016 Special Session on "Evolutionary Fuzzy Systems"

Brief Description:

For more than two decades, evolutionary computation and various meta-heuristics have frequently been used for fuzzy system design under the name of evolutionary fuzzy systems. Their learning and adaptation capabilities enable structure and parameter optimization of fuzzy systems for many kinds of machine learning tasks such as modeling, classification, and rule mining. Their flexible frameworks also enable to handle multiple objectives like accuracy and interpretability maximization and many kinds of data types like imbalanced, missing, and privacy-preserving data sets. The aim of the session is to provide a forum to disseminate and discuss recent and significant research efforts on Evolutionary Fuzzy Systems in order to deal with current challenges on this topic.

Scope and Topics:

The session is open to any high quality submission from researchers working at the particular intersection of evolutionary algorithms and fuzzy systems. The topics of this special session are as follows:
  • Evolutionary Learning/Tuning of Fuzzy Rule-Based Systems
  • Evolutionary Selection of Fuzzy Rules
  • Interpretability-Accuracy Tradeoff 
  • Multiobjective Evolutionary Fuzzy Systems
  • Evolutionary Fuzzy Neural Networks
  • Evolutionary Fuzzy Clustering
  • Swarm Intelligence for Fuzzy Systems
  • Preprocessing and Postprocessing for Evolutionary Fuzzy Systems
  • Applications of Evolutionary Fuzzy Systems to Real World Problems

Also see: http://www.wcci2016.org/spsessions.php

Please submit papers to FUZZ-IEEE 2016 submission site (http://ieee-cis.org/conferences/fuzzieee2016/upload.php).
If you are invited as reviewers of FUZZ-IEEE 2016 and interested in evolutionary fuzzy systems, please select our special session on the review system. You can change your research topics for review from “Edit your contact information”. Please choose “S. Special Sessions > SS-16 Evolutionary fuzzy systems”. Thank you so much in advance for your contributions to our special session.

Organizers:

Yusuke Nojima
Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University
nojima@cs.osakafu-u.ac.jp

Rafael Alcalá
Department of Computer Science and Artificial Intelligence, University of Granada
alcala@decsai.ugr.es

Hisao Ishibuchi
Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University
hisaoi@cs.osakafu-u.ac.jp

Short biography of the organizers:

Yusuke Nojima (M’00) received the B.S. and M.S. Degrees in mechanical engineering from Osaka Institute of Technology, Osaka, Japan, in 1999 and 2001, respectively, and the Ph.D. degree in system function science from Kobe University, Hyogo, Japan, in 2004. Since 2004, he has been with Osaka Prefecture University, Osaka, Japan, where he was a Research Associate and is currently an Associate Professor in Department of Computer Science and Intelligent Systems. He serves as an associate editor of IEEE Computational Intelligence Magazine. He has co-edited five Special Issues at international journals, four of them on the evolutionary fuzzy systems topic. He was Program Co-Chair at GEFS 2010 and General Co-Chair at GEFS 2011 and GEFS 2013. He is a member of the Fuzzy Systems Technical Committee (FSTC) at the IEEE Computational Intelligence Society (CIS), and a chair of the “Evolutionary Fuzzy Systems” Task Force from July 2014. His research interests include multiobjective genetic fuzzy systems, evolutionary multi objective optimization, parallel distributed data mining, and ensemble classifier design. He received the Best Paper Awards from HIS 2006, FUZZ-IEEE 2009 and 2011, WAC 2010, SCIS&ISIS 2010, ACIIDS 2015, etc.

Rafael Alcalá received the M.Sc. degree in Computer Science in 1998 and the Ph.D. degree in Computer Science in 2003, both from the University of Granada, Spain. From 1998 to 2003, he was with Department of Computer Science, University of Jaén. He is currently an Associate Professor in the Department of Computer Science and Artificial Intelligence at the University of Granada, where he is a Member of the Soft Computing and Intelligent Information Systems Research Group. He has published over 90 papers in international journals, book chapters and conferences. He has worked on several research projects supported by the Spanish government and the European Union. As edited activities, he has co-edited six special issues: the IEEE Transactions on Fuzzy Systems special issue on “Genetic Fuzzy Systems: What’s next”, the Evolutionary Intelligence special issue on “Genetic Fuzzy Systems: New Advances” and, the Special Issues on “Evolutionary Fuzzy Systems” at the Soft Computing journal, the International Journal of Computational Intelligence Systems, the International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems and the International Journal on Knowledge-Based Systems. He currently serves as member of the editorial/reviewer board of the journals: IEEE Transactions on Fuzzy Systems (AE), the Scientific World Journal and the Journal of Universal Computer Science. He was a member of the Fuzzy Systems Technical Committee (FSTC) at the IEEE Computational Intelligence Society (CIS) from January 2009 to December 2013, and a chair of the “Evolutionary Fuzzy Systems” Task Force from January 2009 to June 2014. He was Program Co-Chair at GEFS 2010, Area Co-Chair at FUZZ-IEEE 2011 and General Co-Chair at GEFS 2011 and GEFS 2013. His current research interests include multi-objective genetic algorithms and genetic fuzzy systems, particularly the learning/tuning of fuzzy systems for modeling and control with a good trade-off between accuracy and interpretability, as well as fuzzy association rules.

Hisao Ishibuchi (M93-SM10) received the B.Sc. and M.Sc. degrees in precision mechanics from Kyoto University, Kyoto, Japan, in 1985 and 1987, respectively, and the Ph.D. degree in computer science from Osaka Prefecture University, Sakai, Osaka, Japan, in 1992. Since 1987, he has been with Osaka Prefecture University, where he is currently a Professor with the Department of Computer Science and Intelligent Systems. His research interests include fuzzy rule-based classifiers, evolutionary multi- objective optimization, evolutionary fuzzy systems and evolutionary games. He received a Best Paper Award from GECCO 2004, HIS-NCEI 2006, FUZZ- IEEE 2009, WAC 2010, SCIS & ISIS 2010, FUZZ-IEEE 2011, and ACIIDS 2015. He also received the 2007 JSPS Prize from Japan Society for the Promotion of Science. Dr. Ishibuchi was the IEEE CIS Vice-President for Technical Activities (2010-2013). He has been a Chair or Co-Chair of a number of conferences such as Program Chair of IEEE CEC 2010, General Chair of ICMLA 2011, Program Co-Chair of FUZZ-IEEE 2011-2013, Technical Co- Chair of IEEE CEC 2013, Program Co-Chair of IEEE CEC 2014, Program Chair of IES 2014, Publicity Chair of IEEE SSCI 2014, Technical Co-Chair of SEAL 2014, Special Sessions Chair of IEEE CEC 2015, and Program Co- Chair of FUZZ-IEEE 2015. Currently, he is the Editor-in-Chief of IEEE Computational Intelligence Magazine (2014-2015). He is also an Associate Editor of IEEE Trans. on Fuzzy Systems, IEEE Trans. on Evolutionary Computation, IEEE Trans. on Cybernetics, and IEEE Access, and a Steering Committee Member of IEEE Trans. on Autonomous Mental Development. He is an IEEE Fellow (2014).

Sunday 11 October 2015

WCCI 2016 Special Session on Evolutionary Computation in Operations Research, Management Science and Decision Making

Organized by

IEEE CIS Task Force on Intelligent Adaptive Fault Tolerant Control, Reliability, and Optimization

Organizers:

Wei-Chang Yeh, National Tsing Hua University, Taiwan (yeh@ieee.org)
Yew-Soon Ong, School of Computer Engineering Nanyang Technological University, Singapore (ysong@ieee.org)

Aim and Scope

Evolutionary Computation has been shown to attain high quality solutions to difficult optimization problems in fields for which exact and analytical methods do not perform well within tractable time, especially on big-scale problems, since the early 1990s. The essential idea of Evolutionary algorithms lies in the use of  simple agents that work together in leading to emergent global behaviors that solve complex problems efficiently and effectively. In the recent years, there has been increasing interests to create new Evolutionary Computation methodologies by extending from existing Genetic algorithm (GA), Memetic Algorithm (MA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) algorithms, Simplified Swarm Optimization (SSO), and others, that better emulates the power of nature in addressing big-scale real world problems in the field of Operations Research, Management Science and Decision Making. . The developed evolutionary algorithms are expected to be flexible to internal and external changes, robust even when some individuals fail, decentralized and self-organized.

In spite of the significant amount of research on Evolutionary Computation, there remain many open issues and intriguing challenges in addressing big-scale real world problems in the field of Operations Research, Management Science and Decision Making. The aims of this special session are to demonstrate the current state-of-the-art concepts of Evolutionary Computation in the field of Operations Research, Management Science and Decision Making, to reflect on the latest advances and showcase new directions in the area.

Authors are invited to submit their original and unpublished work in the areas including, but not limited to:
  • Evolutionary Computation
  • Advances in Evolutionary Computation
  • Evolutionary Computation applied to all fields of science and technology
  • Novel or Improved frameworks of Evolutionary Computation model
  • Knowledge incorporation in Evolutionary Computation,
  • Neural Networks
  • Fuzzy Systems
  • Multi-objective optimization
  • Robotics
  • Data Mining
  • Green logistic problems
  • Advanced transportation problems
  • Network design
  • Manufacturing cell design
  • Reliability design problems
  • Others

Program Organizers and Chair:

Professor Wei-Chang Yeh, Ph.D.
Department of Industrial Engineering and Engineering Management
National Tsing Hua University, Hsinchu, Taiwan 300
Phone: +886-3-5742443
Fax: +886-3-572-2204
Email: yeh@ieee.org
URL: http://integrationandcollaboration.org
https://sites.google.com/site/integrationcollaborationlab/
Wei-Chang Yeh has completed his Ph.D degree in 1992 at the Department of Industrial Engineering, University of Texas at Arlington, USA. He is the Professor of the Department of Industrial Engineering and Engineering Management in the National Tsing Hua University, Taiwan. He has also published more than 108 papers in reputed journals and serves as an editorial board member of repute. His research interest includes Network Reliability, Cloud Computing Management, SSO and Soft Computing and Data Mining. Prof. Yeh is an editorial board member of “Reliability Engineering and System Safety (RESS)”, “Soft Computing with Applications (SCA)” and “International Journal of management and Marketing (IJMM)”. He is most honored to be able to serve as the Chair for the IEEE Computational Intelligence Society, and looks forward to the event.

Yew-Soon Ong is currently an Associate Professor and Director of Computational Intelligence Graduate Laboratory, Director of the A*Star SIMTECH-NTU Joint Lab on Complex Systems at the Nanyang Technological University, Singapore, and the Programme Principal Investigator of the Rolls-Royce@NTU Corporate Lab. He received his PhD degree on Artificial Intelligence in complex design from the Computational Engineering and Design Center, University of Southampton, United Kingdom in 2003. His current research interest in computational intelligence spans across memetic computation, evolutionary computation, machine learning, Big Data Analytics and agent-based systems.

He is the founding Technical Editor-in-Chief of Memetic Computing Journal, founding Chief Editor of the Springer book series on studies in adaptation, learning, and optimization, Associate Editor of the IEEE Transactions on Evolutionary Computation, the IEEE Transactions on Neural Networks & Learning Systems, IEEE Computational Intelligence Magazine, IEEE Transactions on Cybernetics, IEEE Transactions on Big Data, Soft Computing, International Journal of System Sciences and others. He has coauthored over 200 refereed publications and his research grants in the last five years amounts to a total of more than 25 million Singapore dollars. His research work on Memetic Algorithm was featured by Thomson Scientific's Essential Science Indicators as one of the most cited emerging area of research in August 2007. And he is recipient of the 2015 IEEE Computational Intelligence Magazine Outstanding Paper Award and the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award for his work pertaining to Memetic Computation. Several of his research technologies in memetic computation have been commercialized and licensed to companies and institutions worldwide. Over the last 5 years, he has been invited to deliver over 20 keynote, plenary or lecture speeches at international conferences, workshops and lecture series.
He chaired the IEEE Computational Intelligence Society Emerging Technologies Technical Committee from 2012-2013 and the IEEE Computational Intelligence Society Intelligent Systems and Applications Technical Committee from 2013-2014. Presently, he is Conference Chair of the Congress on Evolutionary Computation, World Congress on Computational Intelligence, Vancouver, Canada, 2016 and also secretary of the IEEE Transactions on Computational Intelligence and AI in Games steering committee.

Program Committee of Potential Participants and Reviewers:

Dr. Changseok Bae
Dr. Vera Yuk Ying Chung
A/Professor Chia-Ling Huang, Ph.D.
A/Professor Yunzhi Jiang, Ph.D.
A/Professor Shirley W.I. Siu, Ph.D.
Dr. Shang-Chia Wei

WCCI 2016 Special Session on "Optimizing Neural Networks via Evolutionary Computation and Swarm Intelligence"

Organized by

IEEE CIS Task Force on Intelligent Adaptive Fault Tolerant Control, Reliability, and Optimization

Organizers:

Wei-Chang Yeh, National Tsing Hua University, Taiwan (yeh@ieee.org)

Liang Feng, College of Computer Science, Chongqing University, China. (liangf@cqu.edu.cn)

Yew-Soon Ong
, School of Computer Engineering Nanyang Technological University, Singapore (ysong@ieee.org)

Scope and motivation

Today, neural networks have been widely recognized as useful frameworks to model multidimensional nonlinear relationships. It has been successfully applied in real-world applications including signal processing, robot control, classification, etc. Recently, it has also been employed to construct deep architectures for deep learning to model high-level abstractions in data, and achieved considerable success in applications such as natural language processing, music signal recognition, computer vision and automatic speech recognition, etc. Despite the success achieved by neural network, constructing multilayer neural networks involves challenging optimization problems, i.e., finding appropriate architecture and the corresponding optimal weights for some of the core applications of interest.  

Evolutionary Computation and Swarm Intelligence are natural inspired heuristic methods with global search capability that have attracted extensive attentions in the last decades. They have been successfully applied to complex optimization problems including continuous optimization, combinatorial optimization, constrained optimization, etc. The aim of this special session is to provide a forum for researchers in the field of neural network to exchange their latest advances in theories, technologies, and practice of optimizing neural networks, especially with deep and large architecture, using evolutionary computation and swarm intelligence.

Relevance for IJCNN

This Special Session on “Optimizing Neural Networks via Evolutionary Computation and Swarm Intelligence” mainly focus on the research of exploring Evolutionary Computation and Swarm Intelligence methodologies for optimizing the neural network architectures. Despite a significant amount of research have been done in neural networks, there remains many open issues and intriguing challenges in optimizing neural network architectures, especially in today's deep learning context, where neural networks usually have many layers and large number of neurons.

Authors are invited to submit their original and unpublished work in the areas including, but not limited to:
  • Evolutionary Computation in Neural Networks,
  • Swarm Intelligence in Neural Networks,
  • Advances in Evolutionary Computation and/or Swarm Intelligence,
  • Knowledge incorporation in Evolutionary Computation and/or Swarm Intelligence,
  • Advances in Neural Networks
  • Analytical studies that enhance our understanding on the behaviors of Evolutionary Computation and/or Swarm Intelligence in optimizing Neural Networks,
  • Novel or Improved frameworks of Neural Networks,
  • Others.

Program Organizers and Chair:

Professor Wei-Chang Yeh, Ph.D.
Department of Industrial Engineering and Engineering Management
National Tsing Hua University, Hsinchu, Taiwan 300
Phone: +886-3-5742443
Fax: +886-3-572-2204
Email: yeh@ieee.org
URL: http://integrationandcollaboration.org
https://sites.google.com/site/integrationcollaborationlab/
Wei-Chang Yeh has completed his Ph.D degree in 1992 at the Department of Industrial Engineering, University of Texas at Arlington, USA. He is the Professor of the Department of Industrial Engineering and Engineering Management in the National Tsing Hua University, Taiwan. He has also published more than 108 papers in reputed journals and serves as an editorial board member of repute. His research interest includes Network Reliability, Cloud Computing Management, SSO and Soft Computing and Data Mining. Prof. Yeh is an editorial board member of “Reliability Engineering and System Safety (RESS)”, “Soft Computing with Applications (SCA)” and “International Journal of management and Marketing (IJMM)”. He is most honored to be able to serve as the Chair for the IEEE Computational Intelligence Society, and looks forward to the event.

Yew-Soon Ong is currently an Associate Professor and Director of Computational Intelligence Graduate Laboratory, Director of the A*Star SIMTECH-NTU Joint Lab on Complex Systems at the Nanyang Technological University, Singapore, and the Programme Principal Investigator of the Rolls-Royce@NTU Corporate Lab. He received his PhD degree on Artificial Intelligence in complex design from the Computational Engineering and Design Center, University of Southampton, United Kingdom in 2003. His current research interest in computational intelligence spans across memetic computation, evolutionary computation, machine learning, Big Data Analytics and agent-based systems.

He is the founding Technical Editor-in-Chief of Memetic Computing Journal, founding Chief Editor of the Springer book series on studies in adaptation, learning, and optimization, Associate Editor of the IEEE Transactions on Evolutionary Computation, the IEEE Transactions on Neural Networks & Learning Systems, IEEE Computational Intelligence Magazine, IEEE Transactions on Cybernetics, IEEE Transactions on Big Data, Soft Computing, International Journal of System Sciences and others. He has coauthored over 200 refereed publications and his research grants in the last five years amounts to a total of more than 25 million Singapore dollars. His research work on Memetic Algorithm was featured by Thomson Scientific's Essential Science Indicators as one of the most cited emerging area of research in August 2007. And he is recipient of the 2015 IEEE Computational Intelligence Magazine Outstanding Paper Award and the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award for his work pertaining to Memetic Computation. Several of his research technologies in memetic computation have been commercialized and licensed to companies and institutions worldwide. Over the last 5 years, he has been invited to deliver over 20 keynote, plenary or lecture speeches at international conferences, workshops and lecture series.
He chaired the IEEE Computational Intelligence Society Emerging Technologies Technical Committee from 2012-2013 and the IEEE Computational Intelligence Society Intelligent Systems and Applications Technical Committee from 2013-2014. Presently, he is Conference Chair of the Congress on Evolutionary Computation, World Congress on Computational Intelligence, Vancouver, Canada, 2016 and also secretary of the IEEE Transactions on Computational Intelligence and AI in Games steering committee.

Liang Feng received the PhD degree from the School of Computer Engineering, Nanyang Technological University, Singapore, in 2014. He was a Postdoctoral Research Fellow at the Computational Intelligence Graduate Lab, Nanyang Technological University, Singapore. He is currently an Assistant Professor at the College of Computer Science, Chongqing University, China. His research interests include Computational and Artificial Intelligence, Memetic Computing, Big Data Optimization and Learning, as well as Transfer Learning.

Program Committee of Potential Participants and Reviewers:

A/Professor Changseok Bae
Professor David W. Coit, Ph.D.
Professor Xiangjian He, Ph.D.
A/Professor Vera Yuk Ying Chung
A/Professor Chia-Ling Huang, Ph.D.
Dr. Gregory Levitin
Professor Ana Maria Madureira, Ph.D.
Professor Shiuhpyng Winston Shieh, Ph.D.
Professor Huaguang Zhang, Ph.D.

Friday 9 October 2015

WCCI 2016 Special Session on Belief Function Theory and Its Applications (FUZZ-IEEE-02)

Scope and Topics


Since its inception, belief function theory, also known as Dempster-Shafer theory or evidence theory, has received growing attention in many fields of applications such as finance, technology, biomedicine, etc, despite of its incompetence in combining belief functions with high conflict. To remove the roadblocks in the development of belief function theory, many improvements have been made subsequently, e.g., the present of transferable belief model (TBM) and Dezert-Smarandache theory (DSmT). At present, more and more researchers are dedicated to studying belief function theory from different views for further exploration and better exploitation.
This special session is intended to provide the latest advances of belief function theory, the relationship between belief function theory and other theories such as probability theory, possibility theory, rough set theory, and fuzzy set theory, the fusion of imperfect information in the united framework of random sets theory, together with their applications in artificial intelligence, to enhance the development of belief function theory for solving problems in engineering. We invite original submissions of high quality in this area. The topics of interest include, but are not limited to:

  • Belief function theory
  • Evidence accumulation
  • Temporal information fusion
  • Conflict management
  • Identification fusion
  • Knowledge discovery
  • Data mining
  • Fuzzy sets
  • Rough sets
  • Random sets
  • Applications of artificial intelligence
Special session papers are treated the same as regular papers and must be submitted via the WCCI 2016 submission website. When submitting choose the “Belief Function Theory and Its Applications” special session from the “Main Research Topic” list. Please follow the regular submission guidelines of WCCI 2016. For the sake of convenience, we list some important information in the following.

Important dates:

Paper Submission Deadline:  January 15, 2016
Paper acceptance notification date:  March 15, 2016
Final paper submission deadline:  April 15, 2016
Conference:  July 25-29, 2016

LaTeX and Word Templates

  • To help ensure correct formatting, please use the style files for U.S. Letter as template for your submission. These include LaTeX and Word.
  • Violations of any of the above paper specifications may result in rejection of your paper. Please note that the Latex template does not allow for keywords. If you are using the Latex template, do not include keywords in your paper.

 

Manuscript Style Information

  • Only papers prepared in PDF format will be accepted.
  • Paper Length: Up to 8 pages, including figures, tables and references. At maximum, two additional pages are permitted with overlength page charge of US$125/page, to be paid during author registration.
  • Paper Formatting: double column, single spaced, #10 point Times Roman font.Margins: Left, Right, and Bottom: 0.75" (19mm). The top margin must be 0.75" (19 mm), except for the title page where it must be 1" (25 mm).
  • No page numbers please. We will insert the page numbers for you.
Note: Violations of any of the above specifications may result in rejection of your paper.

Tuesday 6 October 2015

IEEE Transactions on Fuzzy Systems, Volume 23, Number 5, October 2015

1. Uncertain Random Alternating Renewal Process With Application to Interval Availability
Author(s): Yao, K.; Gao, J.
Page(s): 1333 - 1342

2. Hesitant Fuzzy Linguistic VIKOR Method and Its Application in Qualitative Multiple Criteria Decision Making
Author(s): Liao, H.; Xu, Z.; Zeng, X.
Page(s): 1343 - 1355

3. Quadratic Program-Based Modularity Maximization for Fuzzy Community Detection in Social Networks
Author(s): Su, J.; Havens, T.C.
Page(s): 1356 - 1371

4. Robust Granular Optimization: A Structured Approach for Optimization Under Integrated Uncertainty
Author(s): Wang, S.; Pedrycz, W.
Page(s): 1372 - 1386

5. Adaptive Fuzzy Identification and Control for a Class of Nonlinear Pure-Feedback MIMO Systems With Unknown Dead Zones
Author(s): Liu, Y.; Tong, S.
Page(s): 1387 - 1398

6. Indexing Fuzzy Spatiotemporal Data for Efficient Querying: A Meteorological Application
Author(s): Sozer, A.; Yazici, A.; Oguztuzun, H.
Page(s): 1399 - 1413

7. Dynamic Tanker Steering Control Using Generalized Ellipsoidal-Basis-Function-Based Fuzzy Neural Networks
Author(s): Wang, N.; Er, M.J.; Han, M.
Page(s): 1414 - 1427

8. Fuzzy Reliability Assessment of Systems With Multiple-Dependent Competing Degradation Processes
Author(s): Lin, Y.; Li, Y.; Zio, E.
Page(s): 1428 - 1438

9. A Way to Choquet Calculus
Author(s): Sugeno, M.
Page(s): 1439 - 1457

10. Model Approximation for Fuzzy Switched Systems With Stochastic Perturbation
Author(s): Su, X.; Wu, L.; Shi, P.; Chen, C.L.P.
Page(s): 1458 - 1473

11. An Interval Type-2 Neural Fuzzy Classifier Learned Through Soft Margin Minimization and its Human Posture Classification Application
Author(s): Juang, C.; Wang, P.
Page(s): 1474 - 1487

12. Planning Water Resources Allocation Under Multiple Uncertainties Through a Generalized Fuzzy Two-Stage Stochastic Programming Method
Author(s): Fan, Y.; Huang, G.; Huang, K.; Baetz, B.W.
Page(s): 1488 - 1504

13. Fuzzy Adaptive Output Feedback Tracking Control of VTOL Aircraft With Uncertain Input Coupling and Input-Dependent Disturbances
Author(s): Chwa, D.
Page(s): 1505 - 1518

14. Definite Integrals of Atanassov's Intuitionistic Fuzzy Information
Author(s): Lei, Q.; Xu, Z.; Bustince, H.; Burusco, A.
Page(s): 1519 - 1533

15. Safe Diagnosability of Fuzzy Discrete-Event Systems and a Polynomial-Time Verification
Author(s): Liu, F.
Page(s): 1534 - 1544

16. Bayesian Fuzzy Clustering
Author(s): Glenn, T.C.; Zare, A.; Gader, P.D.
Page(s): 1545 - 1561

17. Enhancing Multiclass Classification in FARC-HD Fuzzy Classifier: On the Synergy Between $n$-Dimensional Overlap Functions and Decomposition Strategies
Author(s): Elkano, M.; Galar, M.; Sanz, J.A.; Fernandez, A.; Barrenechea, E.; Herrera, F.; Bustince, H.
Page(s): 1562 - 1580

18. Type-2 Fuzzy Topic Models for Human Action Recognition
Author(s): Cao, X.; Liu, Z.
Page(s): 1581 - 1593

19. Evolutionary Optimization of a Motorcycle Traction Control System Based on Fuzzy Logic
Author(s): Cabrera, J.A.; Castillo, J.J.; Carabias, E.; Ortiz, A.
Page(s): 1594 - 1607

20. Controller Design for Discrete-Time Descriptor Models: A Systematic LMI Approach
Author(s): Estrada-Manzo, V.; Lendek, Z.; Guerra, T.M.; Pudlo, P.
Page(s): 1608 - 1621

21. IFROWANN: Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification
Author(s): Ramentol, E.; Vluymans, S.; Verbiest, N.; Caballero, Y.; Bello, R.; Cornelis, C.; Herrera, F.
Page(s): 1622 - 1637

22. A Study on Relationship Between Generalization Abilities and Fuzziness of Base Classifiers in Ensemble Learning
Author(s): Wang, X.; Xing, H.; Li, Y.; Hua, Q.; Dong, C.; Pedrycz, W.
Page(s): 1638 - 1654

23. Hesitant Fuzzy Power Bonferroni Means and Their Application to Multiple Attribute Decision Making
Author(s): He, Y.; He, Z.; Wang, G.; Chen, H.
Page(s): 1655 - 1668

24. Dissipativity-Based Sampled-Data Fuzzy Control Design and its Application to Truck-Trailer System
Author(s): Wu, Z.; Shi, P.; Su, H.; Lu, R.
Page(s): 1669 - 1679

25. Dynamical Models of Stock Prices Based on Technical Trading Rules—Part III: Application to Hong Kong Stocks
Author(s): Wang, L.
Page(s): 1680 - 1697

26. Optimization of the Carpool Service Problem via a Fuzzy-Controlled Genetic Algorithm
Author(s): Huang, S.; Jiau, M.; Lin, C.
Page(s): 1698 - 1712

27. A Regularized Monotonic Fuzzy Support Vector Machine Model for Data Mining With Prior Knowledge
Author(s): Li, S.; Chen, C.
Page(s): 1713 - 1727

28. A Stepwise-Based Fuzzy Regression Procedure for Developing Customer Preference Models in New Product Development
Author(s): Chan, K.Y.; Lam, H.K.; Dillon, T.S.; Ling, S.H.
Page(s): 1728 - 1745

29. Approximation-Based Adaptive Fuzzy Tracking Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Time-Delay Systems
Author(s): Wang, H.; Liu, X.; Liu, K.; Karimi, H.R.
Page(s): 1746 - 1760

30. Implementation of an Evolving Fuzzy Model (eFuMo) in a Monitoring System for a Waste-Water Treatment Process
Author(s): Dovzan, D.; Logar, V.; Skrjanc, I.
Page(s): 1761 - 1776

31. Adaptive Personalized Diet Linguistic Recommendation Mechanism Based on Type-2 Fuzzy Sets and Genetic Fuzzy Markup Language
Author(s): Lee, C.; Wang, M.; Lan, S.
Page(s): 1777 - 1802

32. Adaptive Inverse Control of Cable-Driven Parallel System Based on Type-2 Fuzzy Logic Systems
Author(s): Wang, T.; Tong, S.; Yi, J.; Li, H.
Page(s): 1803 - 1816

33. A Model Based on Linguistic 2-Tuples for Dealing With Heterogeneous Relationship Among Attributes in Multi-expert Decision Making
Author(s): Dutta, B.; Guha, D.; Mesiar, R.
Page(s): 1817 - 1831

34. Stabilization and Separation Principle of Networked Control Systems Using the T–S Fuzzy Model Approach
Author(s): Li, H.; Wu, L.; Li, J.; Sun, F.; Xia, Y.
Page(s): 1832 - 1843

35. Decentralized Adaptive Fuzzy Output-Feedback Control of Switched Large-Scale Nonlinear Systems
Author(s): Long, L.; Zhao, J.
Page(s): 1844 - 1860

36. Data-Informed Fuzzy Measures for Fuzzy Integration of Intervals and Fuzzy Numbers
Author(s): Havens, T.C.; Anderson, D.T.; Wagner, C.
Page(s): 1861 - 1875

37. Interval Type-2 Fuzzy Sets are Generalization of Interval-Valued Fuzzy Sets: Toward a Wider View on Their Relationship
Author(s): Sola, H.B.; Fernandez, J.; Hagras, H.; Herrera, F.; Pagola, M.; Barrenechea, E.
Page(s): 1876 - 1882

38. Nonfragile Distributed Filtering for T–S Fuzzy Systems in Sensor Networks
Author(s): Zhang, D.; Cai, W.; Xie, L.; Wang, Q.
Page(s): 1883 - 1890

39. Fuzzy and Set-Valued Stochastic Differential Equations With Local Lipschitz Condition
Author(s): Malinowski, M.T.
Page(s): 1891 - 1898

40. On the Maximum Entropy Negation of a Probability Distribution
Author(s): Yager, R.R.
Page(s): 1899 - 1902

Monday 5 October 2015

Call for Papers Special Session on Transfer Learning in Evolutionary Computation

Data mining, machine learning, and optimisation algorithms have achieved promises in many real-world tasks, such as classification, clustering and regression.  These algorithms can often generalise well on data in the same domain, i.e. drawn from the same feature space and with the same distribution. However, in many real-world applications, the available data are often from different domains. For example, we may need to perform classification in one target domain, but only have sufficient training data in another (source) domain, which may be in a different feature space or follow a different data distribution. Transfer Learning aims to transfer knowledge acquired in one problem domain, i.e. the source domain, onto another domain, i.e. the target domain. Transfer learning has recently emerged as a new learning framework and hot topic in data mining and machine learning.

Aim and Scope: Evolutionary computation techniques have been successfully applied to many real-world problems, and started to be used to solve transfer learning tasks. Meanwhile, transfer learning has attracted increasing attention from many disciplines, and has been used in evolutionary computation to address complex and challenging issues. The theme of this special session is transfer learning in evolutionary computation, covering ALL different evolutionary computation paradigms, including Genetic algorithms (GAs), Genetic programming (GP), Evolutionary programming (EP), Evolution strategies (ES), Learning classifier systems (LCS), Particle swarm optimization (PSO), Ant colony optimization (ACO), Differential evolution (DE), Artificial immune systems (AIS), Evolutionary Multi-objective optimization (EMO), Estimation of distribution algorithms (EDA), and Cultural algorithms (CA).

The aim is to investigate in both the new theories and methods on how transfer learning can be achieved with different evolutionary computation paradigms, and how transfer learning can be adopted in evolutionary computation, and the applications of evolutionary computation and transfer learning in real-world problems. Authors are invited to submit their original and unpublished work to this special session. Topics of interest include but are not limited to:
  • Evolutionary supervised transfer learning
  • Evolutionary unsupervised transfer learning
  • Evolutionary semi-supervised transfer learning
  • Domain adaptation and domain generalization in evolutionary computation
  • Instance based transfer approaches in evolutionary computation
  • Feature based transfer learning in evolutionary computation
  • Parameter/model based transfer learning in evolutionary computation
  • Relational based transfer learning in evolutionary computation
  • Transfer learning in in evolutionary computation for classification
  • Transfer learning in in evolutionary computation for regression
  • Transfer learning in in evolutionary computation for clustering
  • Transfer learning in in evolutionary computation for other data mining tasks, such as association rules and link analysis
  • Transfer learning in in evolutionary computation for scheduling and combinatorial optimisation tasks
  • Hybridisation of evolutionary computation and neural networks, and fuzzy systems for transfer learning
  • Hybridisation of evolutionary computation and machine learning, information theory, statistics, etc., for transfer learning 
  • Transfer learning in in evolutionary computation for real-world applications, e.g. text mining, image analysis, face recognition, WiFi localisation, et al.

Important dates:

  • Paper Submission Deadline: 15 Jan 2016
  • Notification of Acceptance: 15 Mar 2016
  • Final Paper Submission Deadline: 15 Apr 2016

Paper Submission:

Please follow the IEEE WCCI/CEC 2016 Submission Web Site. Special session papers are treated the same as regular conference papers. Please specify that your paper is for the Special Session on Evolutionary Feature Selection and Construction. All papers accepted and presented at WCCI/CEC 2016 will be included in the conference proceedings published by IEEE Explore, which are typically indexed by EI.

Organizers:

Mengjie Zhang
School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand.
Mengjie.Zhang@ecs.vuw.ac.nz; Phone: +64-4-463 5654; Fax: +64-4-463 5045

Muhammad Iqbal
School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand.
Muhammad.Iqbal@ecs.vuw.ac.nz; Phone: +64-4-463 5233+8874; Fax: +64-4-463 5045.

Yi Mei
School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand.
Yi.Mei@ecs.vuw.ac.nz; Phone: +64-4-463 5233+8874; Fax: +64-4-463 5045.

Bing Xue
School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand.
Bing.Xue@ecs.vuw.ac.nz; Phone: +64-4-463 5542 ; Fax: +64-4-463 5045.

Biography of the Organisers

Dr Mengjie Zhang is currently Professor of Computer Science 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 University Research Committee, Associate Dean (Research and Innovation) for Faculty of Engineering, and Chair of the Research Committee of the Faculty and School of Engineering and Computer Science. His research is mainly focused on evolutionary computation, particularly genetic programming, particle swarm optimisation, multi-objective optimisation and learning classifier systems with application areas of classification with unbalanced data, feature selection and dimensionality reduction, computer vision and image processing, job shop scheduling, and bioinformatics. Recently, he has been working on transfer learning, domain adaptation and domain generalization in evolutionary computation and neural networks for classification, regression, scheduling and routing, and computer vision and image processing problems.  He is also interested in data mining, machine learning, and web information extraction. Prof Zhang has published over 350 academic papers in refereed international journals and conferences in these areas. He has been serving as an associated editor or editorial board member for five international journals including IEEE Transactions on Evolutionary Computation, the Evolutionary Computation Journal (MIT Press) and Genetic Programming and Evolvable Machines (Springer), and as a reviewer of over 20 international journals. He has been a general/program/technical chair for eight international conferences. 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 is a senior member of IEEE, Chair of the IEEE CIS Evolutionary Computation Technical Committee, a member of IEEE CIS Intelligent System Applications Technical Committee, a vice-chair of the IEEE CIS Task Force on Evolutionary Computer Vision and Image Processing, and the founding chair of the IEEE Computational Intelligence Chapter in the IEEE New Zealand Central Section.

Dr Muhammad Iqbal completed his PhD in Learning Classifier Systems (LCS) under the supervision of A/Prof Will Browne and Prof Mengjie Zhang, in the Evolutionary Computation Research Group (ECRG), at the School of Engineering and Computer Science, Victoria University of Wellington (VUW), New Zealand. He is currently working as a postdoctoral research fellow at VUW, New Zealand. Iqbal's main research interests are in the area of evolutionary machine learning. His research focuses on evolutionary image analysis and classification using transfer learning in genetic programming and learning classifier systems techniques. Iqbal is currently working on multiclass texture classification using genetic programming by extracting useful knowledge from simpler problems to solve complex problems of the domain. He is also interested in medical image analysis, data mining, and scalability of evolutionary techniques.

Dr Yi Mei (S'09-M'13) is a Research Fellow at the School of Engineering and Computer Science, Victoria University of Wellington, New Zealand. His research interests include evolutionary computation in scheduling, routing and other combinatorial optimization problems. Dr. Mei has a number of top-notch publications in IEEE Transactions on Evolutionary Computation, IEEE Transactions on Systems, Man, and Cybernetics: Part B and ACM Transactions on Mathematical Software. As the sole investigator, he won the 2nd prize of the Competition at IEEE World Congress on Computational Intelligence 2014: Optimisation of Problems with Multiple Interdependent Components. He was the recipient of the 2010 Chinese Academy of Sciences Dean’s Award (top 200 postgraduates all over China) and the 2009 IEEE Computational Intelligence Society (CIS) Postgraduate Summer Research Grant (three to four recipients all over the world). He was ranked top 10% of the unsuccessful applications (near-miss) in ARC DECRA rounds 2014 and 2015. Dr. Mei serves as the committee member of IEEE ECTC Task Force on Evolutionary Scheduling and Combinatorial Optimisation and IEEE CIS Task Force on EC for Feature Selection and Construction.

Dr Bing Xue is currently a Lecturer in Evolutionary Computation Research Group, School of Engineering and Computer Science at Victoria University of Wellington. Her research focuses mainly on evolutionary computation, data mining, and machine learning, particularly, classification, regression, transfer learning and domain adaption, feature selection, feature construction, and multi-objective optimisation. She has over 40 papers published in fully referred international journals and conferences. Dr Xue is the Chair of Task force on Evolutionary Feature Selection and Construction in IEEE Computational Intelligence Society (CIS), Program Co-Chair of the 7th International Conference on Soft Computing and Pattern Recognition (2015), Guest Editor of Special Issue on Evolutionary Optimisation, Feature Reduction and Learning, Soft Computing (Journal), Chair of Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition in IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), Chair of Special session Evolutionary Feature Selection and Construction in IEEE Congress on Evolutionary Computation, WCCI 2016/CEC2016 and CEC2015, and in international conference on Simulated Evolution And Learning (SEAL) 2014. She is also a member of Evolutionary Computation Technical Committee in IEEE CIS. Dr Xue is serving as a reviewer for nearly 20 international journals and a program committee member for over 30 international conferences. She is also serving as the Director of Women in Engineering for the IEEE New Zealand Central Section. Dr Xue is a member of IEEE and IEEE Computational Intelligence Society. She is also serving as the Director of Women in Engineering for the IEEE New Zealand Central Section and the Secretary of the IEEE Chapter on Computational Intelligence in that Section.

IEEE Transactions on Neural Networks and Learning Systems, Volume 26, Issue 10, October 2015

1. Scene Recognition by Manifold Regularized Deep Learning Architecture
Authors: Yuan Yuan; Lichao Mou; Xiaoqiang Lu
Page(s): 2222 - 2233

2. Linear-Time Subspace Clustering via Bipartite Graph Modeling
Authors: Amir Adler; Michael Elad; Yacov Hel-Or
Page(s): 2234 - 2246

3. Nuclear Norm-Based 2-DPCA for Extracting Features From Images
Authors: Fanlong Zhang; Jian Yang; Jianjun Qian; Yong Xu
Page(s): 2247 - 2260

4. Deformed Graph Laplacian for Semisupervised Learning
Authors: Chen Gong; Tongliang Liu; Dacheng Tao; Keren Fu; Enmei Tu; Jie Yang
Page(s): 2261- 2274

5. Learning to Rank for Blind Image Quality Assessment
Authors: Fei Gao; Dacheng Tao; Xinbo Gao; Xuelong Li
Page(s): 2275 - 2290

6. Matrix Variate Distribution-Induced Sparse Representation for Robust Image Classification
Authors: Jinhui Chen; Jian Yang; Lei Luo; Jianjun Qian; Wei Xu
Page(s): 2291 - 2300

7. Optimal Critic Learning for Robot Control in Time-Varying Environments
Authors: Chen Wang; Yanan Li; Shuzhi Sam Ge; Tong Heng Lee
Page(s): 2301 - 2310

8. Adaptive Control of Uncertain Nonaffine Nonlinear Systems With Input Saturation Using Neural Networks
Authors: Kasra Esfandiari; Farzaneh Abdollahi; Heidar Ali Talebi
Page(s): 2311 - 2322

9. Learning-Regulated Context Relevant Topographical Map
Authors: Pitoyo Hartono; Paul Hollensen; Thomas Trappenberg
Page(s): 2323 - 2335

10. Stochastic Stability of Delayed Neural Networks With Local Impulsive Effects
Authors: Wenbing Zhang; Yang Tang; Wai Keung Wong; Qingying Miao
Page(s): 2336 - 2345

11. Energy-to-Peak State Estimation for Markov Jump RNNs With Time-Varying Delays via Nonsynchronous Filter With Nonstationary Mode Transitions
Authors: Lixian Zhang; Yanzheng Zhu; Wei Xing Zheng
Page(s): 2346 - 2356

12. Linear Regression-Based Efficient SVM Learning for Large-Scale Classification
Authors: Jianxin Wu; Hao Yang
Page(s): 2357 - 2369

13. On the Role of Astroglial Syncytia in Self-Repairing Spiking Neural Networks
Authors: Muhammad Naeem; Liam J. McDaid; Jim Harkin; John J. Wade; John Marsland
Page(s): 2370 - 2380

14. Data Imputation Through the Identification of Local Anomalies
Authors: Huseyin Ozkan; Ozgun Soner Pelvan; Suleyman S. Kozat
Page(s): 2381 - 2395

15. Synchronization of Linearly Coupled Networks With Delays via Aperiodically Intermittent Pinning Control
Authors: Xiwei Liu; Tianping Chen
Page(s): 2396 - 2407

16. Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training
Authors: Daniel Soudry; Dotan Di Castro; Asaf Gal; Avinoam Kolodny; Shahar Kvatinsky
Page(s): 2408 - 2421

17. A Kernel Adaptive Algorithm for Quaternion-Valued Inputs
Authors: Thomas K. Paul; Tokunbo Ogunfunmi
Page(s): 2422 - 2439

18. Automatic Face Naming by Learning Discriminative Affinity Matrices From Weakly Labeled Images
Authors: Shijie Xiao; Dong Xu; Jianxin Wu
Page(s): 2440 - 2452

19. Hierarchical Cooperative Control for Multiagent Systems With Switching Directed Topologies
Authors: Jianqiang Hu; Jinde Cao
Page(s): 2453 - 2463

20. Dependent Online Kernel Learning With Constant Number of Random Fourier Features
Authors: Zhen Hu; Ming Lin; Changshui Zhang
Page(s): 2464 - 2476

21. Deep and Shallow Architecture of Multilayer Neural Networks
Authors: Chih-Hung Chang
Page(s): 2477 - 2486

22. Global Synchronization of Complex Dynamical Networks Through Digital Communication With Limited Data Rate
Authors: Yan-Wu Wang; Tao Bian; Jiang-Wen Xiao; Changyun Wen
Page(s): 2487 - 2499

23. Solving Nonlinear Equality Constrained Multiobjective Optimization Problems Using Neural Networks
Authors: Mohammed Mestari; Mohammed Benzirar; Nadia Saber; Meryem Khouil
Page(s): 2500 - 2520

24. Global Nonlinear Kernel Prediction for Large Data Set With a Particle Swarm-Optimized Interval Support Vector Regression
Authors: Yongsheng Ding; Lijun Cheng; Witold Pedrycz; Kuangrong Hao
Page(s): 2521 - 2534

25. Optimal Control of Nonlinear Continuous-Time Systems in Strict-Feedback Form
Authors: Hassan Zargarzadeh; Travis Dierks; Sarangapani Jagannathan
Page(s): 2535 - 2549

26. $ {H}_{ {infty }}$  Tracking Control of Completely Unknown Continuous-Time Systems via Off-Policy Reinforcement Learning
Authors: Hamidreza Modares; Frank L. Lewis; Zhong-Ping Jiang
Page(s): 2550 - 2562

27. Global Neural Dynamic Surface Tracking Control of Strict-Feedback Systems With Application to Hypersonic Flight Vehicle
Authors: Bin Xu; Chenguang Yang; Yongping Pan
Page(s): 2563 - 2575

28. Sparse Representation in Kernel Machines
Authors: Hongwei Sun; Qiang Wu
Page(s): 2576 - 2582

29. Twin Support Vector Machine for Clustering
Authors: Zhen Wang; Yuan-Hai Shao; Lan Bai; Nai-Yang Deng
Page(s): 2583 - 2588

30. Stability Criteria for Recurrent Neural Networks With Time-Varying Delay Based on Secondary Delay Partitioning Method
Authors: Zhanshan Wang; Lei Liu; Qi-He Shan; Huaguang Zhang
Page(s): 2589 - 2595

31. On the Non-STDP Behavior and Its Remedy in a Floating-Gate Synapse
Authors: Roshan Gopalakrishnan; Arindam Basu
Page(s): 2596 - 2601

Friday 2 October 2015

IEEE Transactions on Evolutionary Computation, Volume 19, Number 5, October 2015

1. Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey
Author(s): Tayarani-N., M.; Yao, X.; Xu, H.
Page(s): 609 - 629

2. Solving Uncompromising Problems With Lexicase Selection
Author(s): Helmuth, T.; Spector, L.; Matheson, J.
Page(s): 630 - 643

3. Memetic Search With Interdomain Learning: A Realization Between CVRP and CARP
Author(s): Feng, L.; Ong, Y.; Lim, M.; Tsang, I.W.
Page(s): 644 - 658

4. Computational Cost Reduction of Nondominated Sorting Using the M-Front
Author(s): Drozdik, M.; Akimoto, Y.; Aguirre, H.; Tanaka, K.
Page(s): 659 - 678

5. Building Image Feature Kinetics for Cement Hydration Using Gene Expression Programming With Similarity Weight Tournament Selection
Author(s): Wang, L.; Yang, B.; Wang, S.; Liang, Z.
Page(s): 679 - 693

6. An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition
Author(s): Li, K.; Deb, K.; Zhang, Q.; Kwong, S.
Page(s): 694 - 716

7. Improving Differential Evolution With a Successful-Parent-Selecting Framework
Author(s): Guo, S.; Yang, C.; Hsu, P.; Tsai, J.S.
Page(s): 717 - 730

8. Robust Optimization Over Time: Problem Difficulties and Benchmark Problems
Author(s): Fu, H.; Sendhoff, B.; Tang, K.; Yao, X.
Page(s): 731 - 745

9. A Multioperator Search Strategy Based on Cheap Surrogate Models for Evolutionary Optimization
Author(s): Gong, W.; Zhou, A.; Cai, Z.
Page(s): 746 - 758

Call for Nominations / Applications for the position of the Editor-in-Chief of IEEE Transactions on Cognitive and Developmental Systems

IEEE Transactions on Autonomous Mental Development (TAMD) will be renamed as IEEE Transactions on Cognitive and Developmental Systems (TCDS) with a view to broadening its scope yet keeping main thrust of TAMD in its core. The official scope of TCSD is:

This journal focuses on advances in the study of development and cognition in natural (humans, animals) and artificial (robots, agents) systems. It welcomes  contributions from multiple related  disciplines including cognitive systems, computational intelligence, cognitive robotics, developmental and epigenetic robotics, autonomous and evolutionary robotics, social structures, multi-agent and artificial life systems, computational neuroscience,  and  developmental psychology. Articles on theoretical, computational, application-oriented, and experimental studies as well as reviews in these areas are considered.
Transactions on Cognitive and Developmental Systems is financially sponsored by the IEEE Computational Intelligence Society, Robotics and Automation Society, and the Consumer Electronics Society, and it is technically co-sponsored by the Computer Society. With this new focused title and revised scope, TCDS is going to emerge as a premier publication covering various aspects of Cognitive Systems, Robotics, and Developmental Systems.

TCDS is financially co-sponsored by the Computational Intelligence Society (CIS), Robotics and Automation Society (RAS), and Consumer Electronics Society (CES), and technically co-sponsored by the Computer Society. Following the Memorandum of Understanding (MOU) signed by the co-sponsors, the Editor-in-Chief (EIC) of TCDS will be appointed for a period of two years beginning January 1, 2016. Following the guidelines in the MOU, IEEE CIS Executive Committee has formed an Adhoc Search Committee for recommending to the CIS President a list of potential candidates to serve as EIC of TCDS. 

The Search Committee is seeking nominations /applications for this. The nominees/applicants should be dedicated volunteers with outstanding research profiles and extensive editorial experience. The nomination/application package should include complete CV along with separate description on each of the following items: Vision Statement; Editorial Experience; IEEE-CIS/RAS/CES Publication Experience; IEEE CIS/RAS/CES Volunteer Experience; Leadership Skills; Institutional Support; Networking with Community; Challenges ahead with the publication, if any, and how to deal with them; and Why the candidate considers himself/herself fit for this position.

The nomination/application package should be emailed as a single PDF at nrpal59@gmail.com by October 30, 2015.  

Nikhil R. Pal
On behalf of the Search Committee

Call for Papers & Call for Special Sessions 8th International Conference on Advanced Computational Intelligence

http://icaci.mae.cuhk.edu.hk/
February 14-16, 2016
Chiang Mai, Thailand

Sponsors/organizers:
The Chinese University of Hong Kong, Chiang Mai University, King Mongkut's University of Technology Thonburi

Technical cosponsor:
IEEE Computational Intelligence Society

The Eighth International Conference on Advanced Computational Intelligence (ICACI2016)  will be held in Chiang Mai, Thailand during February 14-16, 2016, as a sequence to IWACI2008 (Macao), IWACI2009 (Mexico City), IWACI2010 (Suzhou), IWACI2011 (Wuhan),  ICACI2012 (Nanjing),  ICACI2013 (Hangzhou), and ICACI2015 (Mount Wuyi).

ICACI2016 aims to provide a high-level international forum for scientists, engineers, and educators to present the state-of-the-art research and applications in computational intelligence. The conference will feature plenary speeches given by world renowned scholars, regular sessions with broad coverage, and special sessions focusing on popular topics. In addition, best paper awards will be given during the conference. The proceedings of ICACI2016 will be submitted to the IEEExplore Database and to be indexed by EI Compendex. Moreover, selected papers will be published in special issues of related journals. The conference will favor papers representing advanced theories and innovative applications in computational intelligence.

Prospective authors are invited to contribute high-quality papers to ICACI2016. In addition, proposals for special sessions within the technical scopes of the conference are solicited. Special sessions, to be organized by internationally recognized experts, aim to bring together researchers in special focused topics. A special session proposal should include the session title, a brief description of the scope and motivation, names, contact information and brief biographical information on the organizers. Researchers interested in organizing special sessions are invited to submit formal proposals to xlhu@mail.tsinghua.edu.cn.

Topics areas include, but not limited to, computational neuroscience, connectionist theory and cognitive science, mathematical modeling of neural systems, neurodynamic analysis, neurodynamic optimization and adaptive dynamic programming, embedded neural systems, probabilistic and information-theoretic methods, principal and independent component analysis, hybrid intelligent systems, supervised, unsupervised and reinforcement learning, deep learning, brain imaging and neural information processing, neuroinformatics and bioinformatics, support vector machines and kernel methods, autonomous mental development, data mining, pattern recognition, time series analysis, image and signal processing, robotic and control applications, telecommunications, transportation systems, intrusion detection and fault diagnosis, hardware implementation, real-world applications, big data processing, fuzzy systems, fuzzy logic, fuzzy set theory, fuzzy decision making, fuzzy information processing, fuzzy logic control, evolutionary computation, ant colony optimization, genetic algorithms, parallel and distributed algorithms, particle swarm optimization, evolving neural networks, evolutionary fuzzy systems, evolving neuro-fuzzy systems, evolutionary games and multi-agent systems, intelligent systems applications.

Important Dates

Special session proposals deadline Oct. 1, 2015
Paper submission deadline Nov. 1, 2015
Notification of acceptance Dec. 1, 2015
Camera-ready copy and author registration Jan. 1, 2016

(PDF version of CFP at http://icaci.mae.cuhk.edu.hk/CFP_ICACI2016.pdf)