Parallel and Distributed Evolutionary Computation in the Cloud Era

Recent trend in distributed systems such as cloud computing, grid computing, and peer-to-peer systems, goes toward a global infrastructure of information resources that can be utilized through the Internet. Especially, infrastructure as a service (IaaS) is becoming popular such as Amazon EC2, S3, and so on, to provide with virtually infinite number of virtual machines and storages just by calling a web-service APIs through the Internet.

In evolutionary computation, environment is considered essential to realize “evolution” since it is necessary to have enough resources and complexities in the environment for the individuals to evolve. Cloud systems may even offer tens of thousands of virtual machines, terabytes of memories and exabytes of storages. Current trend toward many-core architecture increases the number of cores even more dramatically: we may have more than a million of cores to offer extremely massive parallelization.

In this special session, we discuss parallel and distributed evolutionary computation in the cloud era such as implementation of massively parallel evolutionary algorithms employing cloud computing systems and services, parallel implementation of evolutionary algorithms on many-core architectures including GPUs, and we also welcome any types of parallel and distributed evolutionary computation on any “informal” types of computing environment in this special session including the following themes.

• Implementation of parallel and distributed evolutionary computation in cloud computing systems and/or services
• Implementation of massively parallel evolutionary computation on many-core architecture such as GPUs
• Parallel and distributed evolutionary machine learning techniques
• Design and theory of scalable evolutionary algorithms
• Development of parallel and distributed evolutionary computation framework in cloud computing systems
• Applications of parallel and evolutionary computation techniques in cloud or other modern computing environment
• Applications of EC and other bioinspired paradigms to peer to peer systems, and distributed EC algorithms that use them.
• Voluntary, sneaky or parasite computing using the browser or other widely available infrastructure. Zero-cost distributed computing.

Organizers:

Masaharu Munetomo, Information Initiative Center, Hokkaido University, Japan. munetomo@iic.hokudai.ac.jp

Masaharu Munetomo is a professor and vice-director of Information Initiative Center, Hokkaido University, and a chief architect of “Hokkaido University Academic Cloud”, the largest academic cloud system in Japan to conduct research projects realizing a national inter-cloud infrastructure. He has published more than 100 papers in the field of evolutionary computation including advanced evolutionary algorithms based on linkage identification, and distributed systems including cloud computing. He co-organized a special session “EC on Many-core Architecture to Solve Large-scale Problems” in CEC2011 at New Orleans, “Parallel and Distributed Evolutionary Computation in the Cloud Era” in CEC2012 at Cancun, and a member of program committee of CEC since 2002.

Juan Julián Merelo Guervós, department of Computer Architecture and Technology of the University of Granada, Spain.
jmerelo@geneura.ugr.es

Juan Julián Merelo Guervós was born in Úbeda, Jaén, España, in March 10th, 1965. Obtained a degree in Physics (majoring in Theoretical Physics) from the University of Granada in 1988 and his PhD in Physics in 1994 from the same University. He is professor from November 2009, attached to the department of Computer Architecture and Technology of the University of Granada. His research topics are mainly within the area of soft computing, including neural networks, evolutionary algorithms, complex networks and combinations of them. He has been working also on implementations of the above mentioned algorithms using overlay networks and other distributed computing methods, such as peer to peer systems. He has been organizer of several workshops, including one on Informal Distributed Evolutionary Computation at CEC 2011, and coorganizer of several Parallel Problem Solving from Nature, including chairing PPSN 2002 which was held at Granada.

Call for Papers: WCCI 2014 Workshop "Advances in Learning from/with Multiple Learners"

Overview
This workshop will cover original and pioneering contributions, theory as well as applications on creating and combining learning models, and aim at an inspiring discussion on the recent progress and the future developments. Learners based on different paradigms can be combined for improved accuracy. Each learning method presupposes some model of the world that comes with a set of assumptions which may lead to error if they do not hold. Lea
rning is an ill-posed
problem and with finite data each algorithm converges to a different soluti
on and fails under
various circumstances. In learning models combinations, it is possible to make
a distinction
between two main modes: ensemble and modular. For an ensemble approach, several solutions to
the same task, or task component, are combined to yield a more reliable es
timate. In the modular
approach, particular aspects of a task are dealt with by specialist c
omponents before being
recombined to form a global solution. In this workshop, the reasons for combining learning
models and the main methods for creating and combining them will be presente
d. Also, the
effectiveness of these methods will be discussed considering the concept
s of diversity and
selection of these approaches.
The workshop will strive to bring together the practitioners of these approaches i
n an attempt to
study a unified framework under which these interactions can be studied, understood,
and
formalized.
Relevant topics
The following is a partial list of relevant topics (not limited to) for the workshop:
·Bagging approaches
· Boosting techniques
· Collaborative clustering
· Collaborative learning
· Cooperative learning
·Ensemble methods
· Hybrid systems
· Mixtures of distributions
· Mixtures of experts
· Modular approaches
·Multi-task learning
· Multi-view learning
· Task decomposition
· Transfer learning with multiple sources
Submission guidelines and special issue
Prospective authors are invited to submit papers according to the IEEE format.
All submissions
should follow the specifications of WCCI 2014. Manuscripts will be submitt
ed through the IEEE
WCCI 2014 paper submission website and will be subject to the same peer-re
view procedure as
the WCCI2014 regular papers. Accepted contributions will be part of the IJCNN confe
rence
proceedings, which will be available in IEEE Xplore.
Authors of the most insightful papers, already accepted for publication, will be
invited to submit
an extended version of their work to a Special Issue of the Neurocomputing journal (IF: 1.634).
Organizers
· Younès Bennani, Paris 13 University
· Guénael Cabanes, Paris 13 University
·Antoine Cornuéjols, AgroParisTech
· Marc Gelgon, Nantes University
· Nistor Grozavu, Paris 13 University
· Cédric Wemmert, University of Strasbourg
Important Dates
· Submission deadline: December 20, 2013
· Notification of acceptance: March 15, 2014
· Camera-ready deadline: April 15, 2014
·
Workshop date: July 5, 2014
Tentative
Program Committe:
Younès Bennani, Paris 13 University, Villetaneuse, France
Guénael Cabanes, Paris 13 University, Villetaneuse, France
Antoine Cornuéjols, AgroParisTech, Paris, France
Marc Gelgon, Nantes University, Nantes, France
Agostino Gibaldi, University of Genova, Italy
Nistor Grozavu, Paris 13 University, Villetaneuse, France
JunBin Gao, Charles Sturt University, Bathurst, Australia
Tomasz Maszczyk, Nicolaus Copernicus University, Toru
, Poland
Vladimir Nikulin, Vyatka State University, Kirov, Russia
Shogo Okada, Tokyo Institute of Technology, Japan
Seiichi Ozawa, Kobe University, Japan
Asim Roy, Arizona State University, USA
Daming Shi, Middlesex University, London, UK
Tatiana Tambouratzis, University of Piraeus, Piraeus, Greece
Draguna Vrabie, UTRC, USA
Cédric Wemmert, University of Strasbourg, Strasbourg, France

Call for Papers: WCCI 2014 Workshop "Computational Energy Management in Smart Grids"

The International Workshop on Computational Energy Management in Smart Grids (CEMiSG 2014) will be held on July 6th, 2014 in Beijing, China as inside the 2014 IEEE World Congress on Computational Intelligence (WCCI 2014). The Workshop is oriented to explore the new frontiers and challenges within the Computational Intelligence research area, including in particular Neural Networks, Evolutionary Computation and Soft Computing based solutions, for the optimal usage and management of energy resources in Smart Grid applicable scenarios. The Workshop will be a proficient discussion table within the WCCI conference, which attracts the most famous researchers in the Computational Intelligence field worldwide.

ORGANIZERS

• Derong Liu, Chinese Academy of Sciences, China
• Stefano Squartini, Università Politecnica delle Marche, Italy
• Francesco Piazza, Università Politecnica delle Marche , Italy
• Dongbin Zhao, Chinese Academy of Sciences, China
• Haibo He, University of Rhode Island, USA

SCOPE

As the world population increases, the sustainable usage of natural resources becomes an issue that humanity and technology are urgently asked to face. Energy represents a relevant example from this perspective and the strong demand coming from developed and developing countries shoved the scientists worldwide to intensify their studies on renewable energy resources. At the same time, due to the increasing complexity of MV and LV distribution grids on which distributed electrical generators based on renewables have to be included, a growing interest has been oriented to the development of smart systems able to optimally manage the usage and the distribution of energy among the population with the objective of minimizing wasting and the economic impact even at family consumption level. This yielded in a flourishing scientific literature on sophisticated algorithms and systems aimed at introducing intelligence within the energy grid, with also several effective solutions already available in the market. The task is surely challenging and multi-faceted. Indeed the different needs of the heterogeneous grid costumers and the different peculiarities of energy sources to be included in the grid itself have to be taken into account. Moreover several ways of intervention are feasible, as the ones indicated in the US Energy Independence and Security Act of 2007 as reference: self-healing capability, fault-tolerance on resisting attack, integration of all energy generation and storage, dynamic optimization of grid operation and resources with full cyber-security, incorporation of demand-response, demand-side resources and energy-efficient resources, actively client participation in the grid operations by providing timely information and control options, improvement of reliability, power quality, security and efficiency of the electricity infrastructure.

A multi-disciplinary coordinated action is required to the scientific communities operating in the Electrical and Electronic engineering, Computational Intelligence, Digital Signal Processing and Telecommunications research fields to provide adequate technological solutions to these issues having in mind the more and more stringent constraints we have to consider in terms of environment sustainability. In particular, the organizers of this Workshop wants to explore the new frontiers and challenges within the Computational Intelligence research area, including in particular Neural Networks, Evolutionary Computation and Soft Computing based solutions, for the optimal usage and management of energy resources in Smart Grid applicative scenarios.

TOPICS

Workshop topics include, but are not limited to:

• Smart Home Energy Management
• Computational Intelligence for Smart Grids
• Learning Systems for Smart Grid Optimization Tasks
• Neural Networks based algorithms for Complex Energy Systems
• Evolutionary Algorithms in Energy Applications
• Soft Computing in Renewable Energy Systems
• Energy Resource and Task Scheduling
• Building Energy Consumption Forecasting
• Demand-side Management
• Short-term Load Forecasting
• Neural Networks for Time Series Prediction in Smart Grid Applications
• Non-intrusive Electrical Load Analysis
• Hybrid Battery Management
• Brain inspired algorithms for Energy Efficiency

TECHNICAL PROGRAMME COMMITTE

• Pietro Burrascano, University of Terni, Italy
• Zhaohui Hu, Chinese Academy of Sciences, China
• Robert John, University of Nottingham, UK
• Elias Kyriadikes, University of Cyprus, Cyprus
• Andrew Kusiak, University of Iowa, USA
• Chengdong Li,  Shandong Jianzhu University, China
• Kang Li, Queen’s University Belfast, UK
• Honghai Liu, University of Portsmouth, UK
• Sauro Longhi, Polytechnic University of Marche, Italy
• Danilo Mandic, Imperial College, UK
• Stephen G. Matthews, University of Bristol, UK
• Michael Negnevitsky, University of Tasmania, Australia
• Peter Palensky, Austrian Institute of Technology, Austria
• Dianwei Qian, North China Electric Power University, China
• Wei Qiao, University of Nebraska–Lincoln, USA
• Manuel Roveri, Polytechnic of Milan, Italy
• Pierluigi Siano, University of Salerno, Italy
• Gerard Smit, University of Twente, Netherlands
• Dipti Srinivasan, National University of Singapore, Singapore
• Zita Vale, Polytechnic of Porto, Portugal
• Qinglai Wei, Chinese Academy of Sciences, China
• Jinyu Wen, Huazhong University of Science and Technology, China

SUBMISSION GUIDELINES

Prospective authors are invited to submit papers according to the IEEE format. All submissions should be according to the specifications of the IEEE WCCI 2014. Manuscripts will be submitted through the IEEE WCCI 2014 paper submission website and will be subject to the same peer-review review procedure as the IEEE WCCI2014 regular papers. Accepted contributions will be part of the IJCNN conference proceedings, which will be available in IEEE Xplore. A Special Issue of the Neurocomputing journal (IF: 1.634) is also foreseen.

IMPORTANT DATES

• Submission deadline: December 20, 2013
• Notification of acceptance: March 15, 2014
• Camera-ready deadline: April 15, 2014
• Workshop date: July 5, 2014

How to publish your research: Video of Professor Garrison Greenwood

Professor Garrison Greenwood talks about how to publish your research in the IEEE Transactions on Evolutionary Computation. This talk was part of a panel session at the CEC 2013 conference.

Call for Papers: WCCI 2014 Special Session "Complex Fuzzy Sets and Logic"

Organizers: Scott Dick, Dan Tamir

Complex fuzzy sets are an extension to type-1 fuzzy sets in which membership grades are complex numbers. Likewise, complex fuzzy logic is an isomorphic family of multi-valued logics whose truth values are complex numbers. In the ten years since these concepts were first introduced, further theoretical investigations and a number of applications have made complex fuzzy sets and logic a lively and growing research area. This special session will provide a forum to consolidate the community of researchers in this area, share our current ideas, reflect on future directions, and communicate our ideas and vision to the larger Computational Intelligence community. As such, we welcome submissions on all aspects of complex fuzzy sets or complex fuzzy logic, including but not limited to:

• Theory of complex fuzzy logic
• Complex fuzzy sets
• Complex fuzzy inferential systems
• Elicitation of complex fuzzy rules
• Machine learning for complex fuzzy inferential systems
• Hybridizations of complex fuzzy sets and logic with other CI technologies
• Data mining with complex fuzzy sets and logic
• Applications of complex fuzzy sets and logic
• Complex fuzzy logic hardware

AUTHOR INSTRUCTIONS

Papers for the special session are limited to a maximum of 8 pages in length, prepared using the standard WCCI paper templates (double-column format), available at http://www.ieee-wcci2014.org/Paper%20Submission.htm

When you are ready to submit your paper, please proceed to the FUZZ-IEEE submission sub-site at http://ieee-cis.org/conferences/fuzzieee2014/upload.php, and upload your paper there. From the “Main Research Topic” drop-down menu, select “S11. FZ11: Complex Fuzzy Sets and Logic.” Your paper will then proceed through the standard reviewing process for all WCCI papers.

IMPORTANT DATES

All special sessions at WCCI adhere to the same deadlines as regular papers, as follows:
Paper Submission: Dec. 20, 2013
Notification of Acceptance: Mar. 15, 2014
Final Submission: Apr. 15, 2014
Early Registration: Apr. 15, 2014

IEEE Transactions on Neural Networks and Learning Systems: Volume 24, Issue 12, December 2013

1. Canonical Correlation Analysis on Data With Censoring and Error Information
Author(s): Sun, J. ; Keates, S.
Pages: 1909-1919

2. Highly Accurate Moving Object Detection in Variable Bit Rate Video-Based Traffic Monitoring Systems
Author(s): Huang, S.-C. ; Chen, B.-H.
Pages: 1920-1931

3. Recurrent Neural Collective Classification
Author(s): Monner, D.D. ; Reggia, J.A.
Pages: 1932-1943

4. Online Selective Kernel-Based Temporal Difference Learning
Author(s): Chen, X. ; Gao, Y. ; Wang, R.
Pages: 1944-1956

5. Stability and Synchronization of Discrete-Time Neural Networks With Switching Parameters and Time-Varying Delays
Author(s): Wu, L. ; Feng, Z. ; Lam, J.
Pages: 1957-1972

6. Artificial Endocrine Controller for Power Management in Robotic Systems
Author(s): Sauze, C. ; Neal, M.
Pages: 1973-1985

7. Operator Control of Interneural Computing Machines
Author(s): Shih, M.-H. ; Tsai, F.-S.
Pages: 1986-1998

8. Multiple Graph Label Propagation by Sparse Integration
Author(s): Karasuyama, M. ; Mamitsuka, H.
Pages: 1999-2012

9. Universal Blind Image Quality Assessment Metrics Via Natural Scene Statistics and Multiple Kernel Learning
Author(s): Gao, X. ; Gao, F. ; Tao, D. ; Li, X.
Pages: 2013-2026

10. H_{\infty } State Estimation for Complex Networks With Uncertain Inner Coupling and Incomplete Measurements
Author(s): Shen, B. ; Wang, Z. ; Ding, D. ; Shu, H.
Pages: 2027-2037

11. Goal Representation Heuristic Dynamic Programming on Maze Navigation
Author(s): Ni, Z. ; He, H. ; Wen, J. ; Xu, X.
Pages: 2038-2050

12. Accelerated Canonical Polyadic Decomposition Using Mode Reduction
Author(s): Zhou, G. ; Cichocki, A. ; Xie, S.
Pages: 2051-2062

13. Hardware Friendly Probabilistic Spiking Neural Network With Long-Term and Short-Term Plasticity
Author(s): Hsieh, H.-Y. ; Tang, K.-T.
Pages: 2063-2074

14. Neural Network Architecture for Cognitive Navigation in Dynamic Environments
Author(s): Villacorta-Atienza, J.A. ; Makarov, V.A.
Pages: 2075-2087

15. An Equivalence Between Adaptive Dynamic Programming With a Critic and Backpropagation Through Time
Author(s): Fairbank, M. ; Alonso, E. ; Prokhorov, D.
Pages: 2088-2100

16. Semisupervised Multitask Learning With Gaussian Processes
Author(s): Skolidis, G. ; Sanguinetti, G.
Pages: 2101-2112

17. Nonlinear Projection Trick in Kernel Methods: An Alternative to the Kernel Trick
Author(s): Kwak, N.
Pages: 2113-2119

How to publish your research: Video of Professor Kay Chen Tan

A video of a talk by Professor Kay Chen Tan as part of a panel session at the CEC 2013 conference. In this video he describes the IEEE CIS Computational Intelligence Magazine.

Call for Papers: WCCI 2014 Special Session "Applications of Computational Intelligence in Education and Disability to Benefit Society"

Following on from a successful workshop at IEEE WCCI 2008 in Hong Kong, special sessions at FUZZ-IEEE 2009 (Korea),  FUZZ-IEEE 2011 (Taiwan), FUZZ-IEEE 2013 (Hyderabad) and at IEEE WCCI 2010 (Barcelona), IEEE-WCCI 2012 (Brisbane) we are pleased to announce that our special session at IEEE WCCI 2014 has been accepted.

The main objective of the session is to provide a forum to disseminate and discuss recent and significant research efforts in real-world educational and disability focused applications in Computational Intelligence which have significantly benefited society.

A further objective of the session is to show how recent academic research has been transferred into industrial and public organisational environments. Papers should show how the public has effectively engaged with the application and should consider the consider societal implications and public attitudes, alongside others, in the conduct and use of research.

Full details of the special session can be found  at http://wcci2014.com/

Important Deadlines

• Paper submission deadline: December 20, 2013
• Paper acceptance notification date: March 15, 2014 
• Final paper submission deadline: April 15, 2014 
• Early registration: April 15, 2014
• Conference Date: July 6 – 11 2014

IEEE World Congress in Computational Intelligence Website

Paper Submission Instructions

When submitting their manuscripts, authors are recommended to follow these steps:

1. Identify the conference associated to the Special Session they are interested in, by looking at the   "Provisionally Accepted Special Session" list under the column called ID; FOR “Applications of Computational Intelligence in Education and Disability to Benefit Society” the ID is S2:FZ02.
2. Go to the related conference submission website; For papers submitted to this special session the submission site is 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
3. Select the Special Session name in the Main Research topic dropdown list; The special session name is S2:FZ02. Applications of Computational Intelligence in Education and Disability to Benefit Society
4. Fill out the input fields, upload the pdf file and finalize the submission by December 20, 2013

Co-chairs

Keeley Crockett email: K.Crockett@mmu.ac.uk
Bernadette Bouchon-Meunier
Pau-Choo (Julia) Chung
Gary Yen

Call for Papers: Special Session for WCCI 2014: "Computational Intelligence in Bioinformatics"

The special session "Computational Intelligence in Bioinformatics" (CIB) is intended to bring together top researchers, practitioners, and students, from around the world. CIB will serve as a platform to discuss applications of Computational Intelligence in the field of Bioinformatics, Computational Biology, and Bioengineering, for solving problems in medicine, pharmacy, health and life sciences, biology, and forensic sciences.

This CIB special session is sponsored by the IEEE Technical Committee on Bioinformatics and Bioengineering (IEEE BBTC). Further details on the CIB session can be found at:
 http://users.ucg.gr/~vpp/CIB-WCCI-2014/

We cordially invite you to submit research articles in this special session.
We also encourage you to distribute this "call-for-papers" to your
colleagues.

Topics of interest include, but are not limited to:

• Analysis and visualization of large biological data sets
• Biological and medical ontologies
• Biomedical data modelling and mining
• Biomedical model parameterization
• Brain computer interface
• Computational proteomics
• Ecoinformatics and applications to ecological data analysis
• Emergent properties in complex biological systems
• Gene expression array analysis
• Gene finding
• Genetic networks
• High-throughput data analysis
• Immuno- and chemo-informatics
• In-silico optimization of biological systems
• Medical image analysis
• Medical imaging and pattern recognition
• Medicine and health informatics
• Metabolic pathway analysis
• Microarray design or oligonucleotide selection
• Modelling, simulation and optimization of biological systems
• Molecular docking and drug design
• Molecular evolution and phylogenetics
• Molecular sequence alignment and analysis
• Motif and signal detection
• Robustness and evolvability of biological networks
• Single nucleotide polymorphism (SNP) analysis
• Structure prediction and folding
• Systems and synthetic biology and
• Treatment optimization.

WCCI 2014

The 2014 IEEE World Congress on Computational Intelligence (IEEE WCCI 2014) is the largest technical event in the field of computational intelligence. It will host three conferences: the 2014 International Joint Conference on Neural Networks (IJCNN 2014), the 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2014), and the 2014 IEEE Congress on Evolutionary Computation (IEEE CEC 2014).

IEEE WCCI 2014 will be held in Beijing, the capital of the People's  Republic of China. Beijing is the nation's political, economic, and  cultural center as well as China's most important center for international  trade and communications. The congress will provide a stimulating forum for scientists, engineers, educators, and students from all over the world to discuss and present their research findings on computational intelligence. Please visit WCCI 2014 at http://www.ieee-wcci2014.org/

Important dates

Paper submission deadline:          December 20, 2013
Paper acceptance notification date: March 15, 2014
Final paper submission deadline:    April 15, 2014
Early registration Deadline:        April 15, 2014
Conference Dates:                   July 6-11, 2014

Paper Submission

Paper Submission: Electronic web submission in PDF format at the WCCI 2014 website: http://www.ieee-wcci2014.org/

Authors can select the conference through which they will like their  paper(s) to be reviewed and published (if accepted). When an author submits  a paper for a given track of the CIB special session, s/he should:

1. select the conference associated with the given CIB track, and then
2. tag the paper using the CIB name (available from the paper topic roll-down menu) as the first topic. The same paper can only appear in one conference proceeding and it will be available from IEEEXplore (from that proceeding).

Accepted papers will be integrated and presented in a coherent manner in one multi-disciplinary track at WCCI 2014. Each paper will be published in the proceedings of the conference that was selected for submission. The same paper will not appear in three conference proceedings, even if though  the CIB session may span three conferences.

We look forward to seeing you in Beijing, China!

With Best Regards,

Michael Epitropakis
Sheridan Houghten
Michael Lones
Vassilis Plagianakos
Mihail Popescu

Call for Papers: WCCI 2014 Special Session "Advances to Type-2 Fuzzy Logic Control"

Aim

The aim of this special session is to provide a forum for exchanging recent research results in type-2 fuzzy logic control.

Type-2 fuzzy logic control is a paradigm which takes the fundamental concepts in control from type-1 fuzzy logic and expands upon them in order to deal with the high levels of uncertainty present in a vast number of real world control problems. A wide variety of traditional areas in control (that have also been addressed through type-1 fuzzy logic control) have already been addressed with type-2 fuzzy logic, from the control in steel production plants to the control of marine diesel engines and robotic control. In many engineering applications, it has been shown that type-2 fuzzy logic can provide benefits over both traditional forms of control as well as type-1 fuzzy logic and it is the aim of this special session to attract a comprehensive selection of high quality current research in this area of type-2 fuzzy logic control, motivating further collaboration and providing a platform for the discussion on future directions of type-2 fuzzy logic control by researchers active in the field.

Scope

This special session will address advances in interval type-2 as well as general type-2 fuzzy logic control. Topics include, but are not limited to:

• Interval Type-2 Fuzzy Logic Control
• General Type-2 Fuzzy Logic Control
• TSK  Type-2 Fuzzy Logic Control
• Mamdani Type-2 Fuzzy Logic Control
• PID-type Type-2 Fuzzy Logic Controllers
• Model-Based Type-2 Fuzzy Logic Controllers
• Adaptive / Self-Tuning Type-2 Fuzzy Controllers
• Neuro-Fuzzy Type-2 Controllers

Deadline

The deadline for submissions to this special session is 20 December 2013.

Information for Authors

1. Information on the format and templates for papers can be found here: http://www.ieee-wcci2014.org/Paper%20Submission.htm 
2. Papers should be submitted via the FUZZ 2014 paper submission site: http://ieee-cis.org/conferences/fuzzieee2014/upload.php 
3. Select the Special Session name “Advances to Type-2 Fuzzy Logic Control” in the Main Research topic dropdown list
4. Fill out the input fields, upload the PDF file of your paper and finalize your submission by the deadline of December 20, 2013

Organizers

• Dr. Tufan KUMBASAR, Istanbul Technical University, Istanbul, Turkey, kumbasart@itu.edu.tr
• Prof. Engin YESİL, Istanbul Technical University, Istanbul, Turkey, yesileng@itu.edu.tr
• Prof.Hao YING, Wayne State University, Detroit, USA, hao.ying@wayne.edu

Call for Papers: Special Session for WCCI 2014 "Paradigms of Fuzzy Systems for Medical Benefits (FMB)"

Organizers:

Syoji Kobashi, University of Hyogo kobashi [ATMARK] eng.u-hyogo.ac.jp
Md. Atiqur Rahman Ahad, University of Dhaka atiqahad [ATMARK] univdhaka.edu

Aim: 

The purpose of this special session is to disseminate and discuss recent and significant research issues on how Fuzzy systems can be used to solve challenging problems related to medical, biomedical, and healthcare fields. It is held under the IEEE CIS Task Force of “Fuzzy Logic in Medical Sciences”.

Topics of interest: 

• Fuzzy logic-based medical diagnosis control system 
• Fuzzy logic-based medical applications, Soft computing for medical applications 
• Fuzzy logic-based affective computing and psychological evaluations 
• Fuzzy data analysis – bioinformatics, medical informatics, pattern recognition 
• Fuzzy optimization and control 
• Fuzzy machine learning approach to medical applications 
• Fuzzy machine learning approach to biomedical applications 
• Neuro-fuzzy models for biomedical signal processing 
• Signal processing of MRI, fMRI, EEG, ECG, etc. 
• Smart diagnostic predictions of various diseases 
• Applications in image processing and pattern recognition 
• Fuzzy logic vs. other soft computing approaches 
• Approaches based on neuro-fuzzy, evolutionary neuro-fuzzy, neuro-genetic, genetic fuzzy, fuzzy cognitive map 
• Fuzzy inference systems, Assistive robotics 
• Fuzzy temporal representation of knowledge 
• Future trends, Datasets, Healthcare

Important Date:

Last date for submission: 20 Dec. 2013
Paper acceptance notification date: March 15, 2014
Final paper submission deadline: April 15, 2014
Early-registration deadline: April 15, 2014

Submission Instructions:

Manuscripts submitted to the special session FMB should be done through the paper submission website of WCCI 2014 as regular submissions. It is the responsibility of the authors to make sure that papers submitted to the special session clearly indicate the name of the special session the paper belongs to. All papers submitted to the special session FMB will be subject to the same peer-review review procedure as the regular papers.

Please note that this Special Session is specifically & exclusively related to the 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2014). When submitting their manuscripts, authors are recommended to follow these steps:

• Follow the manuscript-style information and templates of regular IEEE WCCI 2014 papers. Visit http://www.ieee-wcci2014.org/Paper%20Submission.htm
• Visit http://ieee-cis.org/conferences/fuzzieee2014/upload.php - select our Special Session from ‘Main research topic*:’ dropdown list.
• Fill out the input fields, upload the pdf file and finalize the submission by December 20, 2013.

Please email to the organizers for any query and after submitting a paper.

Call for Papers: Special Session for WCCI 2014 "Evolutionary Algorithms with Statistical & Machine Learning Techniques"

Introduction to the special session:

An evolutionary optimization algorithm can be viewed as a learning process - it learns properties of the problem in question and locates the optimal solution. Therefore, it is very natural to introduce statistical & machine learning (SML) techniques into evolutionary algorithms (EA). Some examples are the surrogate assist EAs and the EAs by building and sampling probability models, such as estimation of distribution algorithms (EDA), ant colony optimization (ACO), cross-entropy methods, covariance matrix adaptation evolution strategy (CMA-ES), etc. The combination of EAs and SML has been proven to be an efficient strategy for dealing with hard optimization problems. Not only the aforementioned approaches, but also other SML techniques, including regression, density estimation, classification, clustering, and other techniques, can be applied to guide the EA search. This special session aims at bringing researchers who are interested in this area together to review the current state-of-art, exchange the latest ideas and explore future directions.
The major topics of interest include, but are not limited to:

• Theoretical work on EAs with SML
• Experimental studies of EAs with SML
• EAs with SML for multiobjective optimization problems
• EAs with SML in dynamic environments
• EAs with SML for expensive black-box optimization
• Real-world/novel applications

 

Deadline:

The deadline for submissions to this special session is 20 December 2013.

Information for Authors:

• All papers should be submitted electronically through: http://ieee-cis.org/conferences/cec2014/upload.php 
• To submit your papers to the MC special session, please select the Special Session name in the Main Research topic.
• For more submission information please visit: http://www.ieee-wcci2014.org/Paper%20Submission.htm 
• All accepted papers will be published in the WCCI 2014 electronic proceedings, included in the IEEE Xplore digital library, and indexed by EI Compendex.

Organizers:

• Dr. Aimin Zhou, East China Normal University, amzhou@cs.encu.edu.cn
• Prof. John McCall, Robert Gordon University, j.mccall@rgu.ac.uk
• Prof. Qingfu Zhang, University of Essex, qzhang@essex.ac.uk
• Prof. Jose A. Lozano, University of the Basque Country, ja.lozano@ehu.es
• Prof. Pedro Larrañaga, Polytechnic University of Madrid, pedro.larranaga@fi.upm.es

Call for Papers: WCCI 2014 Special Session "Applications of Neural Networks for Financial Modeling and Forecasting"

Aim and scope

The use of neural networks in financial applications has gained enormous popularity in the recent years. By using a data driven empirical analysis, the main goal is to obtain insights into the dynamics of time series and out-of-sample forecasting. Neural networks are widely acknowledged today as an easily “customizable” tool for learning, modeling and studying a lot of problems very difficult to analyze with standard economic models. For instance, they can be used as nonlinear regression models based on a local analysis into clusters, which generalize the standard models used in econometrics and provide an effective tool to capture the main features of price returns, such as fat tails, volatility clustering, persistence, and leverage effects. Some applications focus on the principal processes generating the observed time series and make use of neural networks as nonlinear models that are more suited to identify the behavior of specific prices. On the other hand, rule based neuro-fuzzy systems based on the integration of neural networks and high-level linguistic information, extracted for example by a Web mining process, have been proposed too.

The aim of this Special Session is to promote research and reflect the most recent advances of neural networks, including their hybridization with evolutionary computation, fuzzy systems, metaheuristic techniques and other intelligent methods, in a series of practical problems relevant to the interaction between machine learning and financial modeling and forecasting, the main interest being finalized for searching optimal relationships in the area of financial engineering, risk management, portfolio optimization, industrial organization, auctions, searching equilibriums, financial forecasting, market simulation, agent-based computational economics, and many other areas.

Topics

The topics of interest to be covered by this Special Session include, but are not limited to:

• Financial data mining
• Time series analysis and forecasting
• Soft computing applications
• Dynamics of commodity markets
• Decision support systems
• Risk analysis and credit scoring
• Portfolio management
• Automated trading systems
• Agent-based computational economics
• Economic modeling and finance
• Artificial economics
• Simulation of social processes

Important Dates

December 20, 2013: deadline for paper submission
March 15, 2014: notification to Authors
April 15, 2014: camera-ready deadline for accepted papers
July 6-11, 2014: Conference days

Submission

Manuscripts submitted to Special Sessions should be done through the paper submission website of IEEE WCCI 2014 as regular submissions. All papers submitted to Special Sessions will be subject to the same peer-review review procedure as the regular papers. Special Sessions having fewer than four accepted papers will be cancelled and the accepted papers will be moved to regular sessions.
The Authors intended to contribute to this Special Session are kindly recommended to follow the manuscript style information and templates of regular IEEE WCCI 2014 papers, as described here.
Please note that each Special Session is specifically and exclusively related to one of the three conferences composing the IEEE WCCI 2014, i.e., IJCNN2014, FUZZ-IEEE2014, and IEEE CEC 2014.

When submitting their manuscripts, Authors are recommended to follow these steps:
1. identify the conference associated to the Special Session they are interested in, by looking at the “Provisionally Accepted Special Session” list under the column called ID;
2. go to the related conference submission website;
3. select the Special Session name in the Main Research topic dropdown list;
4. fill out the input fields, upload the PDF file and finalize the submission by December 20, 2013.

Special Session Organizer

MASSIMO PANELLA, Ph.D.
Dept. of Information Engineering, Electronics and Telecommunications
University of Rome “La Sapienza” (Italy)
E-mail: massimo.panella@uniroma1.it

Session Co-Organizer

RITA L. D’ECCLESIA, Ph.D.
Dept. of Methods and Models for Economics, Territory and Finance
University of Rome “La Sapienza” (Italy)
E-mail: rita.decclesia@uniroma1.it

Call for Papers: WCCI 2014 Special Session "Theoretical Foundations of Bio-inspired Computation"

http://www.cs.colostate.edu/~sutton/CEC2014/

Motivation

Bio-inspired search heuristics often turn out to be highly successful for optimization in practice. The theory of these randomized search heuristics explains the success or the failure of these methods in practical applications. Theoretical analyses lead to the understanding of which problems are optimized (or approximated) efficiently by a given algorithm and which are not.
The benefits of theoretical understanding for practitioners are threefold.

• Aiding the algorithm design,
• guiding the choice of the best algorithm for the problem at hand,
• determining the optimal parameter settings.

The theory of evolutionary computation has grown rapidly in recent years. The primary aim of this special session is to bring together people working on theoretical aspects of  bio-inspired computation. The latest breakthroughs in the theory of bio-inspired computation will be reported and new directions will be set.

Scope

Potential authors are invited to submit papers describing original contributions to foundations of evolutionary computation. Although we are most interested in theoretical foundations, computational studies of a foundational nature are also welcome.

The scope of this special session includes (but is not limited to) the following topics:

• Theoretical foundations of bio-inspired heuristics
• Exact and approximation runtime analysis
• Black box complexity
• Self-adaptation
• Population dynamics
• Fitness landscape and problem difficulty analysis
• No free lunch theorems
• Statistical approaches for understanding the behaviour of bio-inspired heuristics
• Computational studies of a foundational nature

All problem domains will be considered including:

• combinatorial and continuous optimization
• single‐objective and multi‐objective optimization
• constraint handling
• dynamic and stochastic optimization
• co‐evolution and evolutionary learning

Paper Submission

You should follow the IEEE CEC 2014 submission website. On the submission system you must select "SS4. EC04: Theoretical Foundations of Bio-inspired Computation" as "Main Research Topic".
Special session papers are treated in the same way as regular conference papers. Click here to visit the main WCCI 2014 web page.

Important Dates

Paper submissions: December 20, 2013
Notification of acceptance: March 15, 2014
Final paper submission: April 15, 2014

Journal Special Issue

Papers of the highest quality amongst those published in this special session will be selected for invitation to a special issue of the “Evolutionary Computation” journal published by MIT Press.

Organisers

Pietro S. Oliveto (Department of Computer Science, The University of Sheffield, UK)  (P.Oliveto@sheffield.ac.uk)
Andrew M. Sutton (Department of Computer Science, Colorado State University) (sutton@cs.colostate.edu)

Previous editions

A previous edition of this special session was successfully organized at CEC 2013  which took place in Cancun, Mexico. The special session attracted 10 submissions of which 3 papers of the highest quality were accepted. Over 50 people attended the special session at the conference. Extended versions of the accepted papers were invited to a special issue of the Theoretical Computer Science (Elsevier) journal entitled “Evolutionary Computation 2013” (edited by T. Friedrich, J. He, T. Jansen, A. Moraglio) containing extensions of the best theoretical work published at evolutionary computation conferences in 2013.

This special session is organised as part of the IEEE CIS Task Force on Theoretical Foundations of Bio-Inspired Computation (http://www.cs.bham.ac.uk/~olivetps/CIStheory.html)

Call for Papers: Special Session for WCCI 2014 "EC Generalisation in High-dimensional Input Spaces"

Scope

Reliable regression/classification models in the field of evolutionary model induction revolve around the fundamental property of generalisation. This ensures that the induced model is a concise approximation of a data-generating process and performs correctly when presented with data that has not been utilised during the learning process. For practical applications of evolutionary model induction one have to deal with input spaces of high dimensionality comprising many input variables. The problem of many input variables is that we would need a large quantity of training data in order to ensure that complex models, necessary to capture complex data dependencies, are fit reliably to the data. Although the curse of dimensionality certainly raises important issues, it has not prevented EC researchers and practitioners to design successful model induction methods. Real data will often be confined to a region of the space having lower effective dimensionality, and in particular the directions over which important variations in the target variables occur may be very confined. This special session addresses the fundamental problem of dimensionality reduction, and its effect on generalisation, in evolutionary model induction techniques. 

Topics Covered:

The major interest is on applications including, but not limited to:

1.    Feature selection methods.
2.    Feature construction methods (i.e. via linear/nonlinear transformations of original features).
3.    Regularisation methods.
4.    Data sampling methods.
5.    Ensemble methods.
6.    Theory of generalisation.
7.    Improvements EC’s generalisation.

Co-chairs

Dr. Ahmed Kattan 
School of Computer Engineering, Um Al Qura University, Saudi Arabia
E-mail: ajkattan@uqu.edu.sa
Homepage: http://www.ahmedkattan.com

Dr. Alexandros Agapitos
Complex and Adaptive Systems Laboratory of University College Dublin (Ireland)
E-mail: alexandros.agapitos@ucd.ie
Home page: http://www.fmc-cluster.org/index.php?option=com_content&view=article&id=114%3Aalexandros-agapitos&catid=55&Itemid=72

How to publish your research: Video of Professor Simon M. Lucas

Professor Simon M. Lucas talks about how to publish research in IEEE Transactions on Computational Intelligence and Artificial Intelligence in Games. This talk was part of a panel session at CEC 2013 and mostly talks about what kind of papers are published in TCIAIG.

Call for Papers: WCCI 2014 Special Session "Advanced Computational Intelligence for Algorithmic Trading"

Motivation:

There is an explosive growth of Algorithmic Trading (i.e., Algo Trading, Program Trading, or Automated Trading) in research and practice over the past few years. As the markets are evolving fast, numerous problems have arisen in the field of Algorithmic Trading due to many reasons. Just a few of them are mentioned below.

First of all, as high volume and high variety heterogeneous data at different frequency, e.g., high and even ultra-high frequency, are exploding in the market, the modeling tasks to explore the big information across from structure to non-structure data have become increasingly complex. Second, with different policy manipulation of market, the market regimes have changed fast in volatility. The demand to achieve robust strategies has even been strong. Finally, as shown in the work by 2013 Nobel laureates, Fama and Shiller, the market is a changing mixture of efficiency and “irrational exuberance”.  It has been interesting and extremely challenging for an investment organization to balance the investment horizons for its portfolio and select different trading strategies to explore and exploit inefficiency and irrationality in the market and then make profits and control risks.

All these kinds of problems attract professionals and researchers to intensively study novel methodology with advanced tools. In many cases, conventional mathematical approaches do not well support the automated trading. Computational/artificial intelligence has the power in adaptively learning the models from data, inferring the market states upon the new information and naturally accounting for the uncertainty. We believe that advanced computational intelligence approaches will mitigate and even someday solve the existing and emerging problems in our trading practice by helping us build up intelligent trading agents.

Goal:

This special session is an approved plan by IEEE CIS Computational Finance and Economics Technical Committee (CFETC) .

It aims to bring practical pioneers and academic researcher together, provide an idea exchanging environment, and explore potential collaboration between industry and academia. In this session, we will explore new theories and solve real trading problems. We also hope to re-exam the Algorithmic Trading state-of-the-art and paradigms under recent data complexity development and policy risks.

Topics:

Topics of interests include, but not limited to, trading models and strategies, risk management, pricing for algorithmic trading, and strategy validation for different underlines in different markets, as follows:

• Connectionist approaches, e.g., neural networks, for learning and approximating price-related functions, market prediction and other novel applications.
• Deep and shallow learning of market structure.
• Bayesian approaches for modeling market factors.
• Non-parametric statistical approaches for trading activities.
• Latent and hidden structure methods for market regime and state identification.
• Transfer learning for information borrowing from different markets and assets.
• Adaptive learning/control for achieving robust strategies.
• Reinforcement learning paradigm for handling and decision-making under high uncertainty.
• Agent-based models and their applications in artificial market and other directions.
• Behavior-based approaches for understanding market inefficiency and irrationality.
• Trading models/agents and strategies evolution.
• Fuzzy system combination with other approaches for inference and decision-making.
• Information theoretic methods and other approaches for portfolio optimization and for optimally allocating capital between trading strategies.
• Utilization of non-structure data and big data in trading practice.
• Validation of algorithmic trading models and strategies.
• Better understanding and control of risk during trading with advanced intelligent modeling.

Information for Authors

This section is part of IEEE International Joint Conference on Neural Network 2014 (IEEE IJCNN 2014) at The IEEE World Congress on Computational Intelligence 2014 (IEEE WCCI 2014).
1)      Information on the format and templates for papers can be found here:
         http://www.ieee-wcci2014.org/Paper%20Submission.htm
2)      Papers should be submitted via the IJCNN 2014 paper submission site:
         http://ieee-cis.org/conferences/ijcnn2014/upload.php
3)      Select the Special Session name in the Main Research topic dropdown list
4)      Fill out the input fields, upload the PDF file of your paper and finalize your submission by the deadline of December 20, 2013

Important dates

Paper submission: 20 December, 2013
Decision: 15 March, 2014
Final paper submission: 15 April, 2014
Conference dates: 6-11 July, 2014

Organizers:

Dr. Chenghui Cai
Global Market Department, Opera Solutions LLC, New York/New Jersey.
E-mail: caichenghui@gmail.com

Dr. Ming Li
Taikang Asset Management Company, Beijing and Taikang Financial Engineering Department.

Dr. Meng Ji
Ernst & Young, New York City.

Prof. Akira Namatame
Department of Computer Science, the Japan Defense Academy (NDA).

Prof. Philip Yu
Department of Statistics and Actuarial Science of The University of Hong Kong.

Prof. Kiyoshi Izumi
School of Engineering, The University of Tokyo

Mr. Robert Golan
DBmind Technologies, USA.

Call for papers: WCCI 2014 Special Session "Complex Networks and Evolutionary Computation"

Introduction to the special session

The application of complex networks to evolutionary computation (EC) has received considerable attention from the EC community in recent years. The most well-known study should be the attempt of using complex networks, such as small-world networks and scale-free networks, as the potential population structures in evolutionary algorithms (EAs). Moreover, the study of using complex networks to analyse fitness landscapes and designing predictive problem difficulty measures is also attracting increasing attentions. On the other hand, using EAs to solve problems related to complex networks, such as community detection, is also a popular topic.

This special session seeks to bring together the researchers from around the globe for a creative discussion on recent advances and challenges in combining complex networks and EAs. The special session will focus on, but not limited to, the following topics:

• Complex networks and fitness landscape analysis
• Complex networks and problem difficulty prediction
• Evolutionary dynamics on complex networks
• Evolutionary algorithms based on complex networks
• Community detection using evolutionary algorithms
• Community detection using multi-objective evolutionary algorithms
• Real world applications of evolutionary algorithms based on complex networks

Submission Guidelines

The authors intended to contribute to IEEE WCCI 2014 Special Sessions are kindly recommended to follow the manuscript style information and templates of regular IEEE WCCI 2014 papers. Please note that each Special Session is specifically and exclusively related to one of the three conferences composing the IEEE WCCI 2014, i.e., IJCNN2014, FUZZ-IEEE2014, and IEEE CEC 2014.
When submitting their manuscripts, authors are recommended to follow these steps:
1. Identify the conference associated to the Special Session they are interested in, by looking at the "Provisionally Accepted Special Session" list under the column called ID;
2. Go to the related conference submission website;
3. Select the Special Session name in the Main Research topic dropdown list (our Special Session name is Evolutionary Computation for Planning and Scheduling);
4. Fill out the input fields, upload the pdf file and finalize the submission by December 20, 2013.

Organizers

Prof. Jing Liu
Affiliation: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, China
Email: neouma@mail.xidian.edu.cn
Home page: http://see.xidian.edu.cn/faculty/liujing/

Call for papers: WCCI 2014 Special Session "Fuzzy Set Theory in Computer Vision"

Fuzzy set theory is the subject of intense investigation in fields like control theory, robotics, biomedical engineering, computing with words, knowledge discovery, remote sensing and socioeconomics. However, in the area of computer vision, other fields, e.g., machine learning, and communities, e.g., PAMI, ICCV, CVPR, ECCV, NIPS, are arguably the state-of-the-art. In particular, the vast majority of top performing techniques on public datasets are steeped in probability theory. Important questions to the fuzzy set community include the following. What is the role of fuzzy set theory in computer vision? Does fuzzy set theory make the biggest impact in terms of low-, mid- or high-level computer vision? Furthermore, do current performance measures favor machine learning approaches? Is there additional benefit that fuzzy set theory brings, and if so, how is it measured?

This special session invites new research in fuzzy set theory in computer vision. It is a follow up to the 2013 FUZZ-IEEE workshop View of Computer Vision Research and Challenges for the Fuzzy Set Community. In particular, we encourage authors to investigate their research using public datasets and to compare their results to both fuzzy and non-fuzzy methods. Topics of interest include all areas in computer vision and image/video understanding. Example topics include, but are not limited to, the following:

• Detection and recognition
• Categorization, classification, indexing and matching
• 3D-based computer vision
• Advanced image features and descriptors
• Motion analysis and tracking 
• Linguistic description and summarization
• Video: events, activities and surveillance
• Intelligent change detection
• Face and gesture
• Low-level, mid-level and high-level computer vision
• Data fusion for computer vision
• Medical and biological image analysis
• Vision for Robotics

Interested authors intended to contribute to this special sessions are kindly recommended to follow the manuscript style information and templates of regular IEEE WCCI 2014 papers, as described in here. When submitting the manuscripts, authors are recommended to follow these steps:
1.    Select this Special Session name (FZ 13 – Fuzzy Set Theory in Computer Vision) in the Main Research topic dropdown list;
2.    Fill out the input fields, upload the pdf file and finalize the submission by December 20, 2013.

Organizers:

Chee Seng Chan (Uni. of Malaya)                   
James Keller  (Uni. of Missouri)
Derek T Anderson (Mississippi State Uni.)      
Tony Xu Han (Uni. of Missouri)

Call for papers: WCCI 2014 Special Session "Lattice Computing"

Aim and scope

During the last ten years, a novel analysis of fuzzy intervals based on lattice theory has paved the way for novel extensions of major computational intelligence paradigms including the: fuzzy inference systems, fuzzy adaptive resonance theory and self-organizing maps. Novelties include 1) accommodation, in principle, of granular system inputs, 2) computing with words, and 3) introduction of tunable nonlinearities. Evolutionary computation is often employed for tuning performance.

In addition, lattice theory is also used instrumentally in different domains including logic as well as reasoning. Likewise, lattice theory is used in mathematical morphology toward signal processing as well as in formal concept analysis toward knowledge-representation. Lately, the term Lattice Computing, or LC for short, has been introduced as “an evolving collection of tools and methodologies that process lattice-ordered data including logic values, numbers, sets, symbols, graphs, etc”. In the aforementioned sense, LC emerges with the potential of unifying rigorously the treatment of disparate types of data either separately or jointly in any combination.

This special session is meant as a forum for researchers with interests in LC. The objective is to present high-quality, state-of-the-art research results. An array of novel mathematical tools, design practices and real world applications will be presented. Emphasis is on reasoning, knowledge-representation, signal processing, system modeling, clustering, classification and the cross-fertilization of different technologies. Topics of interest include but are not limited to

• Lattice algebra neural networks 
• Fuzzy lattice reasoning 
• Fuzzy adaptive resonance theory 
• Mathematical morphology 
• Implications 
• Similarity measures 
• System modeling 
• Probabilistic reasoning 
• Granular computing
• Computing with words
• Data mining
• Disparate data fusion
• Semantic Web
• Knowledge representation
• Formal concept analysis
• Algebraic logic
• Multi-valued logic
• Spatial and temporal logic
• Automated reasoning
• Application of proof theory
• Pattern recognition

 

Organizers

Professor Vassilis KABURLASOS
Department of Computer & Informatics Engineering
TEI of Eastern Macedonia & Thrace
Agios Loukas 65404 Kavala, Greece
Email: vgkabs@teikav.edu.gr
His “google scholar” WebPage is at http://scholar.google.com/citations?user=3RiPf3wAAAAJ

Professor Manuel GRAÑA
Department of Computer Science and Artificial Intelligence
Universidad del Pais Vasco (UPV/EHU)
Paseo Manuel Lardizabal 1, 20018 Donostia-San Sebastian, Spain
Email: manuel.grana@ehu.es
His “google scholar” WebPage is at http://scholar.google.com/citations?hl=es&user=lM0Hb4wAAAAJ

Professor Yang XU
College of Mathematics
Southwest Jiaotong University
Chengdu 610031, Sichuan, P. R. China
Email: xuyang@home.swjtu.edu.cn

Call for papers: WCCi 2014 Special Session "Data Mining and Machine Learning Meet Evolutionary Computation"

Introduction to the special session:

Evolutionary Computation (EC), such as Genetic Algorithm(GA), Genetic Programming(GP) , Particle Swarm Optimization(PSO) and the like, has been widely applied to many aspects in the fields of data mining and machine learning, mostly 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 in these two aspects, yet, there remain many open issues and opportunities that are continually emerging as intriguing challenges for the field. 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 Compuation
• 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://ieee-cis.org/conferences/cec2014/upload.php

To submit your papers to the MC special session, please select the Special Session name in the Main Research topic.

For more submission information please visit: http://www.ieee-wcci2014.org/Paper%20Submission.htm
All accepted papers will be published in the WCCI 2014 electronic proceedings, included in the IEEE Xplore digital library, and indexed by  EI Compendex.

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 at the Shantou University, China.  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 a lecturer with College of Computer Science and Technology, Nanjing University of Aeronautics&Astronautics. His main research interests include evolutionary computation, multi-objective optimization, constrained optimization and relevant real-world application.


Jun Zhang
Sun Yat-Sen University, Guangzhou, China.
E-Mail: issai@mail.sysu.edu.cn
Jun Zhang (M’02–SM’08) received the Ph.D. degree in electrical engineering from the City University of Hong Kong, Kowloon, Hong Kong, in 2002. From 2003 to 2004, he was a Brain Korean 21 Post-Doctoral Fellow with the Department of Electrical Engineering and Computer Science, Korea Advanced Institute of Science and Technology, Daejeon, Korea. Since 2004, he has been with Sun Yat-Sen University, Guangzhou, China, where he is currently a Cheung Kong Professor with the Department of Computer Science. He has authored seven research books and book chapters, and over 100 technical papers in his research areas. His current research interests include computational intelligence, cloud computing, high performance computing, data mining, wireless sensor networks, operations research, and power electronic circuits. Dr. Zhang was a recipient of the China National Funds for Distinguished Young Scientists from the National Natural Science Foundation of China in 2011 and the First-Grade Award in Natural Science Research from the Ministry of Education, China, in 2009. He is currently an Associate Editor of the IEEE Transactions on Evolutionary Computation, the IEEE Transactions on Industrial Electronics, the IEEE Transactions on Cybernetics, and the IEEE Computational Intelligence Magazine. He is the Founding and Current Chair of the IEEE Guangzhou Subsection and IEEE Beijing (Guangzhou) Section Computational Intelligence Society Chapters.

K. C. Tan
Department of Electrical and Computer Engineering, National University of Singapore, Singapore
Mail: eletankc@nus.edu.sg
TAN Kay Chen received the B. Eng degree with First Class Honors in Electronics and Electrical Engineering, and the Ph.D. degree from the University of Glasgow, Scotland, in 1994 and 1997, respectively. He is actively pursuing research in computational and artificial intelligence, with applications to multi-objective optimization, scheduling, automation, data mining, and games. Dr Tan has published over 100 journal papers, over 100 papers in conference proceedings, co-authored 5 books including Multiobjective Evolutionary Algorithms and Applications (Springer-Verlag, 2005), Modern Industrial Automation Software Design (John Wiley, 2006; Chinese Edition, 2008), Evolutionary Robotics: From Algorithms to Implementations (World Scientific, 2006; Review), Neural Networks: Computational Models and Applications (Springer-Verlag, 2007), and Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms (Springer-Verlag, 2009), co-edited 4 books including Recent Advances in Simulated Evolution and Learning (World Scientific, 2004), Evolutionary Scheduling (Springer-Verlag, 2007), Multiobjective Memetic Algorithms (Springer-Verlag, 2009), and Design and Control of Intelligent Robotic Systems (Springer-Verlag, 2009). Dr Tan has been invited to be an invited keynote/plenary speaker for over 30 international conferences. He served in the international program committee for over 100 conferences and involved in the organizing committee for over 40 international conferences, including the General Co-Chair for IEEE Congress on Evolutionary Computation 2007 in Singapore. Dr Tan is the General Co-Chair for IEEE World Congress on Computational Intelligence 2016 in Vancouver, Canada. Dr Tan is an IEEE Distinguished Lecturer of IEEE Computational Intelligence Society since 2011. Dr Tan is currently the Editor-in-Chief of IEEE Computational Intelligence Magazine (CIM). He also serves as an Associate Editor / Editorial Board member of over 20 international journals, such as IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, IEEE Transactions on Computational  Intelligence and AI in Games, Evolutionary Computation (MIT Press), European Journal of Operational Research, Journal of Scheduling etc. Dr Tan is the awardee of the 2012 IEEE Computational Intelligence Society (CIS) Outstanding Early Career Award for his contributions to evolutionary computation in multi-objective optimization. He also received the Recognition Award (2008) from the International Network for Engineering Education & Research (iNEER) for his outstanding contributions to engineering education and research.

Qingfu 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 multiobjective 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.

Call for papers: WCCI 2014 Special Session "Evolutionary Multi-objective Optimization"

Organizers:

Sanaz Mostaghim, University of Magdeburg, Germany sanaz.mostaghim@ovgu.de
Kalyanmoy Deb, Michigan State University, USA, kdeb@msu.edu

Scope:

This special session invites papers discussing recent advances in the development and application of biologically-inspired multi-objective optimization algorithms.

Many problems from science and industry have several (and normally conflicting) objectives that have to be optimized at the same time. Such problems are called multi-objective optimization problems and have been subject of research in the past two decades. One of the reasons why evolutionary algorithms are so suitable for multi-objective optimization is because they can generate a whole set of solutions (the Pareto-optimal solutions) in a single run rather than requiring an iterative one-solution-at-a-time process as followed in traditional mathematical programming techniques.

The main aim of this special session organized within the 2014 IEEE Congress on Evolutionary Computation (CEC'2014) is to bring together both experts and new-comers working on Evolutionary Multi-objective Optimization (EMO) to discuss new and exciting issues in this area.

We encourage submission of papers describing new concepts and strategies, and systems and tools providing practical implementations, including hardware and software aspects. In addition, we are interested in application papers discussing the power and applicability of these novel methods to real-world problems in different areas in science and industry. You are invited to submit papers that are unpublished original work for this special session at CEC 2014. The topics are, but not limited to, the following

1. Many-objective optimization
2. Theoretical aspects of EMO algorithms 
3. Real-world applications of EMO algorithms
4. Test and benchmark problems for EMO algorithms 
5. New EMO techniques including those using meta-heuristics such as artificial immune systems, particle swarm optimization, differential evolution, cultural algorithms, etc. 
6. Multi-objectivization and visualization techniques
7. Handling practicalities, such as uncertainty, noise, constraints, dynamically changing problems, bi-level problems, mixed-integer problems, computationally expensive problems, fixed budget of evaluations, etc.
8. Performance measures for EMO algorithms
9. Techniques to keep diversity in the population 
10. Comparative studies of EMO algorithms 
11. Memetic and Metaheuristics based EMO algorithms 
12. Hybrid approaches combining, for example, EMO algorithms with mathematical programming techniques and exact methods
13. Parallel EMO approaches 
14. Adaptation, learning, and anticipation
15. Evolutionary multi-objective combinatorial optimization, EMO control problems, EMO inverse problems, EMO data mining, EMO machine learning

More Information about EMO including papers, dissertations, test problems and general up-to-date information can be found on the EMOO repository webpage: 
http://delta.cs.cinvestav.mx/~ccoello/EMOO/

Author's Schedule

For the deadline for submitting papers, please check the website of WCCI 2014:
http://www.ieee-wcci2014.org/

IEEE Transactions on Fuzzy Systems: Volume 21, Issue 5, October 2013

1. Fuzzy-Model-Based Fault-Tolerant Design for Nonlinear Stochastic Systems Against Simultaneous Sensor and Actuator Faults
Author(s): Ming Liu ; Xibin Cao ; Peng Shi
Page(s): 789-799

2. Stability Analysis of Polynomial-Fuzzy-Model-Based Control Systems Using Switching Polynomial Lyapunov Function
Author(s): Lam, H.K. ; Narimani, M. ; Hongyi Li ; Honghai Liu
Page(s): 800-813

3. Hierarchical Clustering Problems and Analysis of Fuzzy Proximity Relation on Granular Space
Author(s): Xu-Qing Tang ; Ping Zhu
Page(s): 814-824

4. RFRR: Robust Fuzzy Rough Reduction
Author(s): Suyun Zhao ; Hong Chen ; Cuiping Li ; Mengyao Zhai ; Xiaoyong Du
Page(s): 825-841

5. Model Checking of Linear-Time Properties Based on Possibility Measure
Author(s): Yongming Li ; Lijun Li
Page(s): 842-854

6. Clustering Spatiotemporal Data: An Augmented Fuzzy C-Means
Author(s): Izakian, H. ; Pedrycz, W. ; Jamal, I.
Page(s): 855-868

7. Conditional Density Estimation Using Probabilistic Fuzzy Systems
Author(s): van den Berg, J. ; Kaymak, U. ; Almeida, R.J.
Page(s): 869-882

8. Robust Stability and Stabilization of Uncertain T–S Fuzzy Systems With Time-Varying Delay: An Input–Output Approach
Author(s): Lin Zhao ; Huijun Gao ; Karimi, H.R.
Page(s): 883-897

9. Multiary α-Resolution Principle for a Lattice-Valued Logic
Author(s): Yang Xu ; Jun Liu ; Xiaomei Zhong ; Shuwei Chen
Page(s): 898-912

10. Adaptive Fuzzy Decentralized Output Feedback Control for Nonlinear Large-Scale Systems With Unknown Dead-Zone Inputs
Author(s): Shaocheng Tong ; Yongming Li
Page(s): 913-925

11. Chaos-Based Fuzzy Regression Approach to Modeling Customer Satisfaction for Product Design
Author(s): Huimin Jiang ; Kwong, C.K. ; Ip, W.H. ; Zengqiang Chen
Page(s): 926-936

12. Induction of Shadowed Sets Based on the Gradual Grade of Fuzziness
Author(s): Tahayori, H. ; Sadeghian, A. ; Pedrycz, W.
Page(s): 937-949

13. A Genetic Fuzzy Linguistic Combination Method for Fuzzy Rule-Based Multiclassifiers
Author(s): Trawinski, K. ; Cordon, O. ; Sanchez, L. ; Quirin, A.
Page(s): 950-965

14. Network-Based Robust Passive Control for Fuzzy Systems With Randomly Occurring Uncertainties
Author(s): Zheng-Guang Wu ; Peng Shi ; Hongye Su ; Jian Chu
Page(s): 966-970

15. A Simple Fuzzy Method to Remove Mixed Gaussian-Impulsive Noise From Color Images
Author(s): Camarena, J.-G. ; Gregori, V. ; Morillas, S. ; Sapena, A.
Page(s): 971-977

16. Proximity-Based Clustering: A Search for Structural Consistency in Data With Semantic Blocks of Features
Author(s): Pedrycz, W.
Page(s): 978-982

17. A Note on Fuzzy Relational Equations With Min-Implication Composition
Author(s): Pingke Li
Page(s): 983-986

18. Comments on “Quantized Control Design for Impulsive Fuzzy Networked Systems”
Author(s): Guotao Hui ; Jun Yang ; Bonan Huang
Page(s): 987

IEEE Transactions on Neural Networks and Learning Systems: Volume 24, Issue 11, November 2013

1. Error Surface of Recurrent Neural Networks
Author(s): Phan, M.C. ; Hagan, M.T.
Pages: 1709-1721
 
2. Single-Channel Blind Separation Using Pseudo-Stereo Mixture and Complex 2-D Histogram
Author(s): Tengtrairat, N. ; Gao, B. ; Woo, W.L. ; Dlay, S.S.
Pages:  1722-1735

3. On the SVMpath Singularity
Author(s): Dai, J. ; Chang, C. ; Mai, F. ; Zhao, D. ; Xu, W.
Pages: 1736-1748

4. Multistability of Two Kinds of Recurrent Neural Networks With Activation Functions Symmetrical About the Origin on the Phase Plane
Author(s): Zeng, Z. ; Zheng, W.X.
Pages: 1749-1762

5. Safety-Aware Semi-Supervised Classification
Author(s): Wang, Y. ; Chen, S.
Pages: 1763-1772

6. Neural Network Approaches for Noisy Language Modeling
Author(s): Li, J. ; Ouazzane, K. ; Kazemian, H.B. ; Afzal, M.S.
Pages: 1773-1784

7. A New Discrete-Continuous Algorithm for Radial Basis Function Networks Construction
Author(s): Zhang, L. ; Li, K. ; He, H. ; Irwin, G.W.
Pages: 1785-1798

8. Finding Potential Support Vectors in Separable Classification Problems
Author(s): Varagnolo, D. ; Del Favero, S. ; Dinuzzo, F. ; Schenato, L. ; Pillonetto, G.
Pages: 1799-1813

9. Nonlinear Systems Identification and Control Via Dynamic Multitime Scales Neural Networks
Author(s): Fu, Z.-J. ; Xie, W.-F. ; Han, X. ; Luo, W.-D.
Pages: 1814-1823

10. Hierarchical Similarity Transformations Between Gaussian Mixtures
Author(s): Rigas, G. ; Nikou, C. ; Goletsis, Y. ; Fotiadis, D.I.
Pages: 1824-1835

11. Negative Correlation Ensemble Learning for Ordinal Regression
Author(s): Fernandez-Navarro, F. ; Gutierrez, P.A. ; Hervas-Martinez, C. ; Yao, X.
Pages: 1836-1849

12. Online Learning of a Dirichlet Process Mixture of Beta-Liouville Distributions Via Variational Inference
Author(s): Fan, W. ; Bouguila, N.
Pages: 1850-1862

13. Transfer Ordinal Label Learning
Author(s): Seah, C.-W. ; Tsang, I.W. ; Ong, Y.-S.
Pages: 1863-1876

14. Pseudo-Orthogonalization of Memory Patterns for Associative Memory
Author(s): Oku, M. ; Makino, T. ; Aihara, K.
Pages: 1877-1887

15. Multilabel Classification Using Error-Correcting Codes of Hard or Soft Bits
Author(s): Ferng, C.-S. ; Lin, H.-T.
Pages: 1888-1900

16. Multiclass Support Vector Machines With Example-Dependent Costs Applied to Plankton Biomass Estimation
Author(s): Gonzalez, P. ; Alvarez, E. ; Barranquero, J. ; Diez, J. ; Gonzalez-Quiros, R. ; Nogueira, E. ; Lopez-Urrutia, A. ; del Coz, J.J.
Pages: 1901-1905

17. Corrections to: “Estimator Design for Discrete-Time Switched Neural Networks With Asynchronous Switching and Time-Varying Delay”
Author(s): Zhang, D. ; Yu, L. ; Wang, Q.-G. ; Ong, C.
Pages:  1906

Call for Papers: WCCI 2014 Special Session "Intelligent Design for Reliable Cloud Computing"

Aim

The rapid progress in science and technology over the recent decade has provided a foundation for the development of advanced complex systems or networks. However, the increasing complexities and sophistications of the problems that we attempt to deal with today have led to new emerging challenges pertaining to design, implementation and evaluation of reliable complex systems or networks.

Today, cloud computing has become an increasingly important driving platform for business strategies and real world applications. In particular, it is now a major driving force for complex social, financial, environmental, cultural, and organisational events and activities. The reliability of cloud computing is particularly essential and beginning to garner new attentions. Recent incidents pertaining to the failure of mobile network services are excellent examples that illustrate the importance of system reliability in cloud computing. Disruptions in the Hotmail and Gmail services have resulted in significant email data loss by customers. The unintentional leak of sensitive personal information by Facebook has also violated user privacy. The reliability of cloud computing is thus an important but challenging topic that is required to be dealt with a great care. 

For cloud computing, network reliability is a critical issue affecting its overall performance. Network reliability is generally defined as the probability that a given amount of data is successfully delivered from a set of source nodes to a set of sink nodes under particular conditions. The network reliability problem is Non-deterministic Polynomial-time hard (NP-Hard). The computational effort of this problem grows exponentially with the increase of the number of nodes and the number of links in a network. A modern cloud may consist of thousands of nodes, which makes many well-known algorithms and innovative methods impracticable to estimate network reliability within limited time. Notably, cloud computing is dynamic, as network parameters (e.g., bandwidth or speed) often change quickly. Therefore, there is always a need to evaluate and predict the performance reliability of cloud computing against time-variant responses

To solve NP-Hard problems and to achieve better quality solutions, modern computational intelligence (e.g., evolutionary, Swarm intelligent and artificial neural network) have been proposed for complex optimization problems. Despite a significant amount of research on computational intelligence, there remain many open issues and intriguing challenges in the field. The aims of this symposium are to reflect on the latest advances in intelligent design for reliable network, to explore the emerging or future directions of reliability research and cloud computing in computational intelligence, and to raise the awareness of the computing community at large on this effective technology of intelligent design for reliable cloud computing. Specifically, we endeavor to demonstrate the current state-of-the-art concepts, theory and practice of intelligent and reliable design.

Scope

• Topics relevant to this special session include, but are not limited to, the following applications of computational intelligence, including Artificial Neural Networks, Fuzzy Systems, and Evolutionary Algorithms: Novel competitive, collaborative and cooperative frameworks of clouding computing model,
• Analytical and/or theoretical studies that enhance our understanding on the behaviors of Intelligent Design, 
• Intelligent Redundancy allocation Design,
• Formal and Probabilistic Single/Multi-Objective Intelligent Design, 
• Cognitive, Brain, individual learning, and social learning inspired Intelligent Design,
• Partial or full or meta-Lamarckian/Baldwinian, meta-learning, agent based computational intelligence in designing reliable clouding computing,
• Computational intelligence frameworks using surrogate or approximation methods in designing reliable clouding computing, 
• Computational intelligence for computationally expensive problems and real-world applications,
• Knowledge incorporation in clouding computing.

Deadline

The deadline for submissions to this special session is 20 December 2013.

Information for Authors

1)    Information on the format and templates for papers can be found here:
       http://www.ieee-wcci2014.org/Paper%20Submission.htm
2)    Papers should be submitted via the IJCNN 2014 paper submission site:
       http://ieee-cis.org/conferences/ijcnn2014/upload.php
3    Select the Special Session name in the Main Research topic dropdown list
4)    Fill out the input fields, upload the PDF file of your paper and finalize your submission by the deadline of December 20, 2013

Organisers

• Dr. Wei-Chang Yeh, National Tsing Hua University, Taiwan. yeh@ieee.org
• Dr. G. Levitin, The Israel Electric Corp., Israel. levitin@iec.co.il
• Dr. Hoang Pham, Rutgers, United State. hopham@rci.rutgers.edu
• Dr. Jiankun Hu, University of New South Wales, Australia. J.Hu@adfa.edu.au
• Dr. Jinjun Chen, University of Technology, Sydney. Jinjun.Chen@uts.edu.au

Call for Papers: WCCI 2014 Special Session "Evolutionary Developmental Systems"

Motivation and Scope

Computational modeling of biological development has received increasing interests in evolutionary computation, artificial life and computational systems biology. In the evolutionary computation community, evolutionary algorithms using an indirect coding or generative coding are believed to be more scalable in evolving highly complex systems, compared to those using a direct coding. A variety of developmental and generative models have been proposed, ranging from a set of re-writing rules to gene regulatory network model including metabolic reactions.

Evolutionary  developmental  systems  (Evo-Devo)  are  of  great  interest  not  only  to  efficient optimization of large-scale systems, but also to the understanding of the evolution of the body plan and neural control in living systems. This special session aims to promote cross-disciplinary research in evolutionary computation, artificial life, computational neuroscience and computational systems biology. Topics of the special session include but are not limited to:

• Scalable evolutionary algorithms using indirect or generative encoding
• Evolution of computational models for morphological development
• Evolutionary development of neural systems, neural plasticity
• Morphogenetic self-organizing systems, morphogenetic robotics
• Analysis of evolvability and robustness of developmental systems
• Evolutionary reconstruction of gene regulatory networks
• Benchmarking evolutionary developmental systems

Submission:

All papers are to be submitted electronically at:     http://ieee-cis.org/conferences/cec2014/upload.php. Please choose “Special Session on Evolutionary Developmental Systems" as the topic of your paper. Please also notify the organizers of your submission. Submission deadline: December 20, 2013.

Special Session Organizers:

Dr Hyondong Oh
Department of Computing, University of Surrey
Guildford, GU2 7XH, United Kingdom
Email:  h.oh@surrey.ac.uk

Prof. Yaochu Jin
Department of Computing, University of Surrey
Guildford, GU2 7XH, United Kingdom
Email:  yaochu.jin@surrey.ac.uk

Prof. Larry Bull
Department of Computer Science and Creative Technologies
University of the West of England Bristol, BS16 1QY, United Kingdom
Email:  Larry.Bull@uwe.ac.uk