Important Dates for 2025 IEEE Conference on Evolutionary Computation

CEC 2025 will be held in Hangzhou, China, June 8-12 2025. Some important dates are:

• Special Session & Workshop Proposals: 15 November 2024
• Competition & Tutorial Proposals: 15 December 2024
• Paper Submission: 15 January 2025

CFP: CEC 2019 workshop "Understanding of Evolutionary Optimization Behavior"

CALL FOR PAPERS

We are organising the workshop “Understanding of Evolutionary Optimization Behavior (UEOB 2019)” at the IEEE Congress on Evolutionary Computation 2019 (http://cec2019.org/programs/workshops.html#cec-02) in Wellington, New Zealand.

Please consider to contribute to and/or forward to the appropriate groups the following opportunity to present original research articles in CEC 2019.

SCOPE

The focus of the UEOB 2019 is to highlight theoretical and empirical research that investigates approaches needed to analyze stochastic optimization algorithms and performance assessment with regard to different criteria. The main goal is to bring the problem and importance of understanding optimization algorithms closer to researchers and to show them how and why this is important for future development in the optimization community. This will help researchers/users to transfer the gained knowledge from theory into the real world, or to find the algorithm that is best suited to the characteristics of a given real-world problem.

More detailed information can be found at http://cs.ijs.si/ueob/.

TOPICS OF INTEREST

• Data-driven approaches (machine learning/information theory/statistics) for assessing algorithm performance
• Vector embeddings of problem search space
• Meta-learning
• New advances in analysis and comparison of algorithms
• Operators influence on algorithm behavior
• Parameters influence on algorithm behavior
• Theoretical algorithm analysis

SUBMISSION GUIDELINES

All submissions should be formatted according to the CEC 2019 submission guidelines provided at http://www.cec2019.org/papers.html#submission. 

All submissions will be handled through EasyChair (https://easychair.org/conferences/?conf=ueob2019) and reviewed by the program committee.

In order to participate to this workshop, a full or a student registration at CEC 2019 is required.

Selected papers will be invited to be extended for a special issue in Natural Computing (https://link.springer.com/journal/11047).

IMPORTANT DATES

• Paper submission: 15 March, 2019
• Notification to authors: 31 March, 2019
• Early registration: 31 March, 2019
• Final submission: 15 April, 2019
• Conference: 10-13 June, 2019

ORGANIZERS

Tome Eftimov

Department of Biomedical Data Sciences, Stanford Medicine

Stanford University

USA

Peter Korošec

Computer Systems Department

Jožef Stefan Institute

Slovenia

Christian Blum

Artificial Intelligence Research Institute (IIIA)

Spanish National Research Council (CSIC)

Spain

Call for Papers CEC 2017

We are pleased to announce that the 2017 IEEE Conference on Evolutionary Computation (www.cec2017.org)  will be held in San Sebastian, Spain, in June 5-8, 2017.

IEEE CEC  is a world-class conference that aims to bring together researchers and practitioners in the field of evolutionary computation and computational intelligence from all around the globe. Technical exchanges within the research community will encompass keynote lectures, regular and special sessions, tutorials, and competitions as well as poster presentations. In addition, participants will be treated to a series of social functions, receptions, and networking to establish new connections and foster everlasting friendship among fellow counterparts.

Donostia/San Sebastian is a coastal medium-size city located in northern Spain, 20km from the border with France. It stands as an amphitheatre over-looking the sea. It is known the world over for its spectacular bay and is referred to as the Pearl of the Cantabrian Sea. It is also internationally renowned for being a culinary haven where visitors will find more Michelin stars per square meter than in any other city in the world. The conference will take place at the Kursaal Convention Center and Auditorium. Kursaal is located in the city center, overlooking the seafront. This avant-garde architectural showpiece was designed by Rafael Moneo  and won the Mies van der Rohe prize for the best building in Europe in 2001. The main hotels, restaurants and shopping areas are within walking distance.

CEC 2017 covers all topics in the field of Evolutionary Computation including the following non-exhaustive list:

• Genetic algorithms
• Genetic programming
• Estimation of distribution algorithms
• Evolutionary programming
• Evolution strategies
• Bioinformatics and bioengineering
• Coevolution and collective behavior
• Combinatorial and numerical optimization
• Constraint and uncertainty handling
• Evolutionary data mining
• Evolutionary learning systems
• Evolvable/adaptive hardware and systems
• Evolving neural networks and fuzzy systems
• Evolutionary multi-objective optimization
• Ant colony optimization
• Artificial life
• Agent-based systems
• Molecular and quantum computing
• Particle Swarm Optimization
• Artificial immune systems
• Representation and operators
• Industrial applications of EC
• Evolutionary game theory
• Cognitive systems and applications
• Computational finance and economics
• Estimation of distribution algorithms
• Evolutionary design
• Evolutionary scheduling

Important Dates:

Submission deadline for special sessions: November 7, 2016

Deadline for the submission of tutorials and competitions proposals: January  9, 2017

Paper submission deadline: January 16, 2017   

Paper Decision notification: February 26, 2017


All the papers have to be submitted electronically through the congress application.

For Program inquiries please contact the Program Chair, Carlos Coello at ccoello@cs.cinvestav.mx.

General inquiries for IEEE CEC 2017 should be sent to the General Chair, Jose A. Lozano at  info@cec2017.org

You can also follow CEC 2017 on Twitter: https://twitter.com/cec2017

We are looking forward to seeing you in San Sebastian!

Call for Papers CEC2015 Special Session "Evolutionary Computation in Operations Research, Management Science and Decision Making"

Organizers:

Wei-Chang Yeh, National Tsing Hua University, Taiwan (yeh@ieee.org)
Yew-Soon Ong, School of Computer Engineering, Director of the Centre for Computational Intelligence, Nanyang Technological University, Singapore (ysong@ieee.org)
Vera Yuk Ying Chung, School of Information Technologies, The University of Sydney, Australia (vchung@it.usyd.edu.au)
Changseok Bae, Electronics and Telecommunications Research Institute (ETRI), Korea (csbae@etri.re.kr)

Aim and Scope

Evolutionary Computation has roots in Darwin’s theory of survival of the fittest and Artificial Intelligence respectively. The essential idea of Evolutionary Computation algorithms is to employ many simple agents applying almost no rule which in turn leads to an emergent global behavior. That is, Evolutionary Computation is the emergent collective intelligence of groups of simple agents. Evolutionary Computation are initialized with a population of random solutions inside the problem space and it then searches for optimal solutions by updating generations. There are several popular Evolutionary Computation algorithms based on these concepts, including Genetic algorithm (GA), Memetic Algorithm (MA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) algorithms, Simplified Swarm Optimization (SSO), and many other flavors.

Since the early 1990s, Evolutionary Computation has been utilized to obtain optimal or good-quality solutions to difficult optimization problems in fields for which exact and analytical methods do not produce optimal solutions in an acceptable amount of time, especially for these problems are very difficult to solve by conventional approaches. With Evolutionary Computation, the developed algorithms are flexible to internal and external changes, robust when some individuals fail, and decentralized and self-organized.

It is recognized the Evolutionary Computation have been a popular research area that has received significant attention during the past several decades because of Evolutionary Computation’s critical importance in various kinds of fields. In recent years, we have seen an increasing interest in Evolutionary Computation in creating stochastic methodologies and optimization techniques with the aims of resembling and simulating the phenomenon of nature for solving larger problems in Operations Research, Management Science and Decision Making including:

• Multi-objective optimization
• Fuzzy optimization problems
• Combinatorial optimization problems
• Scheduling problems
• Green logistic problems
• Advanced transportation problems
• Network design and routing
• Manufacturing cell design
• Reliability design problems

Furthermore, the Evolutionary Computation is the mainstream of data mining analyzing and discovering knowledge from a large complex dataset of heterogeneous quality. The huge datasets exist in modern society so that makes many well-known algorithms and innovative methods impracticable to explore the optimization information by mining data.

Despite a significant amount of research on Evolutionary Computation, there remain many open issues and intriguing challenges in the field. The aims of this special session are to demonstrate the current state-of-the-art concepts of Evolutionary Computation in Operations Research, Management Science and Decision Making, to reflect on the latest advances in Evolutionary Computation, and to explore the future directions in Evolutionary Computation.

Authors are invited to submit their original and unpublished work in the areas including, but not limited to:

• Evolutionary Computation,
• The studies of Evolutionary Computation in Operations Research, Management Science or Decision Making,
• Novel or Improved frameworks of Evolutionary Computation model,
• Data Mining using Evolutionary Computation,
• Analytical studies that enhance our understanding on the behaviors of Evolutionary Computation,
• The optimization techniques of Evolutionary Computation,
• Knowledge incorporation in Evolutionary Computation,
• 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 include Network Reliability, Scheduling Problem, Cloud Computing Management, SSO and Soft Computing and Data Mining. Prof. Yeh is an editorial board members 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.

Program Committee of Potential Participants and Reviewers:

• Professor Huaguang Zhang, Ph.D. 
• Professor Ana Maria Madureira, Ph.D.
• Professor David W. Coit, Ph.D.
• A/Professor Binyue Cui, Ph.D.
• Professor Xiangjian He, Ph.D.
• A/Professor Chia-Ling Huang, Ph.D.
• A/Professor Yunzhi Jiang, Ph.D.
• Professor Ji-Hyun Lee, Ph.D.
• Dr. Gregory Levitin
• Professor Shiuhpyng Winston Shieh, Ph.D.
• Dr. Shang-Chia Wei A/Profeesor Xiaoding Yue, Ph.D.
• Dr. Mei-Chi Chuang

Call for Papers CEC2015 Special Session "Evolutionary Computation in Dynamic and Uncertain Environments"

AIMS AND SCOPE

Many real-world optimization problems are subject to dynamism and uncertainties that are often  impossible to avoid in practice. For instance, the fitness function is uncertain or noisy  as a result of simulation/ measurement errors or approximation errors (in the case where surrogates  are used in place of the computationally expensive high fidelity fitness function). In addition,  the design variables or environmental conditions can be perturbed or they change over time.

The tools to solve these dynamic and uncertain optimization problems (DOP) should be flexible,  able to tolerate uncertainties, fast to allow reaction to changes and adaptive. Moreover, the  objective of such tools is no longer to simply locate the global optimum solution, but to  continuously track the optimum in dynamic environments, or to find a robust solution that  operates properly in the presence of uncertainties.

The last decade has witnessed increasing research efforts on handling dynamic and uncertain  optimization problems using evolutionary algorithms and other metaheuristics, and a variety of  methods have been reported across a broad range of application backgrounds.

This special session aims at bringing together researchers from both academia and industry to  review the latest advances and explore future directions in this field.

Topics of interest include but are not limited to:

• Benchmark problems and performance measures
• Dynamic single - and multi-objective optimization
• Adaptation, learning, and anticipation
• Models of uncertainty and their management
• Handling noisy fitness functions
• Using fitness approximations
• Searching for robust optimal solutions
• Algorithm comparison and benchmarking
• Hybrid approaches
• Theoretical analysis
• Real-world applications

IMPORTANT DATES:

Paper submission:        December 19, 2014
Notification:            February 20, 2015
Final paper submission:  March 13, 2015

INFORMATION FOR AUTHORS:

1. Information on the format and templates for papers can be found here: http://sites.ieee.org/cec2015/paper_submission/
2. Papers should be submitted via the CEC 2015 paper submission site: http://sites.ieee.org/cec2015/paper-submission/
3. Select SS10 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 19, 2014.

ORGANIZERS:

Dr Michalis Mavrovouniotis: De Montfort University, United Kingdom
email: mmavrovouniotis@dmu.ac.uk

Dr Changhe Li: China University of Geosciences, Wuhan, China.
email: changhe.lw@gmail.com.

Prof Shengxiang Yang: De Montfort University, United Kingdom
email: syang@dmu.ac.uk

Prof Yinan Guo: China University of Mining and Technology, China
email: guoyinan@cumt.edu.cn

Call for Papers CEC2015 Special Session "Computational Intelligence and Games"

Special Session for IEEE CEC 2015.

 This special session is organized in association with the IEEE Computational Intelligence Society Technical Committee on Games.

Aim

Games are an ideal domain to study computational intelligence (CI) methods because they provide affordable, competitive, dynamic, reproducible environments suitable for testing new search algorithms, pattern-based evaluation methods, or learning concepts. They are also interesting to observe, fun to play, and very attractive to students. Additionally, there is great potential for CI methods to improve the design and development of both computer games and non-digital games such as board games. This special session aims at gathering not only leading researchers, but also young researchers as well as practitioners in this field who research applications of computational intelligence methods to computer games.

Scope

In general, papers are welcome that consider all kinds of applications of CI methods (evolutionary computation, supervised learning, unsupervised learning, fuzzy systems, game-tree search, etc.) to games (card games, board games, mathematical games, action games, strategy games, role-playing games, arcade games, serious games, etc.). Examples include

• Adaptation in games
• Automatic game testing 
• Coevolution in games
• Comparative studies (e.g. CI versus human-designed players)
• Dynamic difficulty in games.
• Games as test-beds for CI algorithms
• Imitating human players
• Learning to play games
• Multi-agent and multi-strategy learning
• Player/opponent modelling
• Procedural content generation
• Results of game-based CI competitions
• Results of open competitions

Submission Guidelines

Special session papers should be uploaded online through the paper submission website of IEEE CEC 2015 by December 19, 2014. Please select the corresponding special session name ("Computational Intelligence and Games") as the “main research topic” in submission.For the latest information on important dates, please refer to this page.

Organizers

Ruck Thawonmas
Professor, Dept. of Human and Computer Intelligence, Ritsumeikan University, Japan
ruck@ci.ritsumei.ac.jp

Daniel Ashlock
Professor, Dept. of Mathematics and Statistics, University of Guelph, Canada
dashlock@uoguelph.ca

Biographies

1. Dr. Ruck Thawonmas is Professor at the Department of Human and Computer Intelligence, College of Information Science and Engineering, Ritsumeikan University in Japan, where he leads the Intelligent Computer Entertainment Laboratory. His research interests include game AI, metaverse, and player-behavior analysis. His laboratory has won a number of game AI competitions with the most recent one at AIIDE 2014 StarCraft AI Competition. He also organized a fighting game AI competition at CIG 2014 and is now serving as Associate Editor for IEEE Transactions on Computational Intelligence and AI in Games (TCIAIG).
2. Dr. Daniel Ashlock is a Professor in the Department of Mathematics and Statistics at the University of Guelph in Canada.  Dr. Ashlock's work in games includes a large number of publications in mathematical games as well as a number of papers on automatic content generation.  Dr. Ashlock is an Associate Editor of TCIAIG, the leading CI-games journal.  He is a longstanding member of the CIS Games Technical Committee, and has severed on the Organizing Committee of the Computational Intelligence in Games conference six times including serving as general chair in 2013.

Call for Papers CEC2015 Special Session "Evolutionary Computer Vision"

Computer vision is a major unsolved problem in computer science and engineering. Over the last decade there has been increasing interest in using evolutionary computation approaches to solve vision problems. Computer vision provides a range of problems of varying difficulty for the development and testing of evolutionary algorithms.

The theme proposed special session is the use of evolutionary computation for solving computer vision and image processing problems. This special session aims to bring together theories and applications of evolutionary computation to computer vision and image processing problems. Authors are invited to submit their original and unpublished work to this Special Session. Topics of interest include but are not limited to

New theories and methods in different EC paradigms to computer vision and image processing including

• Evolutionary algorithms such as Genetic algorithms, genetic programming, evolutionary strategy and evolutionary programming;
• Swarm Intelligence such as particle swarm optimisation, ant colony optimisation, and differential evolution; and
• Other approaches such as learning classifier systems, harmony search, and artificial immune systems.  Cross-fertilization of evolutionary computation and other techniques such as neural networks and fuzzy systems is also encouraged.

Applications in computer vision and image processing including

• Edge detection in noisy images
• Image segmentation in biological images
• Automatic feature extraction, construction and selection in complex images
• Object identification and scene analysis for medical applications
• Object detection and classification in security scenarios
• Handwritten digit recognition and detection
• Vehicle plate detection
• Face detection and recognition
• Texture image analysis
• Automatic target recognition in military services
• Gesture identification and recognition
• Robot vision

Important dates:

•  Paper submission: 19 Dec 2014
• Acceptance notification: 20 Feb 2015
• Final paper submission: 13 Mar 2015

Special Session Organizers:

Mengjie Zhang, School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand. 
Email: mengjie.zhang@ecs.vuw.ac.nz
Homepage: http://homepages.ecs.vuw.ac.nz/~mengjie/

Mengjie Zhang is Professor of computer science at Victoria University of Wellington, New Zealand, where he is heading the interdisciplinary Evolutionary Computation Research Group. He has been working in the area of evolutionary computer vision and signal processing for over 10 years. He has over 300 publications in international conferences and journals including over 100 in evolutionary computer vision and has been supervising over 50 research students in this area. He is the Chair of IEEE CIS Evolutionary Computation Technical Committee, a member of IEEE CIS Intelligent Systems and Application Technical Committee, an Associate Editor or Editorial Board for five international journals including IEEE Transactions on Evolutionary Computation, the Evolutionary Computation Journal (MIT Press) and Genetic Programming and Evolvable Machines (Springer). He is also a Vice-Chair of the Task Force on Evolutionary computer vision and image processing (IEEE CIS EC Technical Committee) and the founding Chair of the IEEE Chapter on Computational Intelligence in New Zealand (Central Section).


Vic Ciesielski, School of Computer Science and Information Technology, RMIT University, City Campus, GPO 2476V, VIC, Australia.
Email: vic.ciesielski@cs.rmit.edu.au
Homepage: http://www.cs.rmit.edu.au/~vc/

Vic Ciesielski is an Associate Professor of computer science at RMIT University. He has been working in the area of evolutionary computer vision for over 10 years. He has over 100 publications in international conferences and journals including more than 40 on various aspects of evolutionary computer vision. He has supervised six PhD students in this area. He is also a member of the Task Force on Evolutionary Computer Vision and Image Processing (IEEE CIS EC Technical Committee).


Mario Koeppen, Graduate School of Creative Informatics, Kyushu Institute of Technology, 680-4, Kawazu, Iizuka, Fukuoka 820-8502 JAPAN.
Email: mkoeppen@ieee.org
Homepage: http://science.mkoeppen.com/

Mario Köppen is a professor at Kyuhsu Institute of Technology, Japan. He has also been working in the field of applied image processing within the scope of industrial projects for more than ten years. His research is focused on the use of soft computing technologies, esp. evolutionary computation, neural networks and fuzzy fusion, for the design of image processing applications. He has over 150 publications in international conferences, journals and book chapters, including a large number of publications that are strongly related to the topic of this proposal.  He is also a Vice-Chair of the Task Force on Evolutionary computer vision and image processing (IEEE CIS EC Technical Committee).

Call for Papers CEC2015 Special Session "Evolutionary Computation for Music, Art, and Creativity"

Special Session for IEEE CEC 2015.


http://cilab.cs.ccu.edu.tw/ci-tf/ECMAC2015.html

This special session extends our previous events at IEEE CIS conferences:

• Special Session on Evolutionary Computation for Creative Intelligence at CEC 2013
• IEEE Symposium on Computational Intelligence for Creativity and Affective Computing (CICAC 2013) at SSCI 2013
• Special Session on Evolutionary Computation for Creative Intelligence at CEC 2012
• Workshop in Evolutionary Music at CEC 2011

Organizers:

This special session is organized by the co-chairs of IEEE CIS ETTC Task Force on Creative Intelligence.

Francisco Fernández de Vega   

University of Extremadura, Spain    
Francisco Fernández is Associate Professor at the University of Extremadura. He received his BS from the University of Seville 1993, MS from the University of Seville 1997, and Ph. D from the University of Extremadura 2001. His research interests include Parallel and Distributed Evolutionary Algorithms and their applications to multiple aspects of art and design. He's been guest editor with Soft Computing, Parallel Computing, Journal of Parallel and Distributed, Natural Computing and edited the books Parallel and Distributed Computational Intelligence and Parallel Architectures and Bioinspired Algorithms, with Springer. He is cochair of EvoPar, part of Evo* Conference. He has published more than 200 papers in conferences and journals.  His work was recently awarded with the 2013 ACM GECCO Art, Design and Creativity Competition.

Palle Dahlstedt   

University of Gothenburg, Sweden   
Palle Dahlstedt is active both as a researcher in the field of computational creativity, and as an internationally recognized composer and improviser. He is associate professor in computer-aided creativity at the Dept. of Applied Information Technology, University of Gothenburg & Chalmers University of Technology, Sweden, and main lecturer in electronic and computer music and artistic director of the Lindblad Studios at the Academy of Music and Drama, University of Gothenburg. His music has been performed on six continents, and received prizes such as the prestigeous Gaudeamus Prize 2001. He has published extensively within the field, and is currently directing a major research project around technology-based creativity in musical performance.

Chuan-Kang Ting   

National Chung Cheng University, Taiwan   
Chuan-Kang Ting (S’01–M’06¬–SM’13) received the B.S. degree from National Chiao Tung University, Taiwan, in 1994, the M.S. degree from National Tsing Hua University, Taiwan, in 1996, and the Ph.D. degree from the University of Paderborn, Germany, in 2005. He is currently an Associate Professor at the Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan. His research interests are in evolutionary computation, computational intelligence, metaheuristic algorithms, and their applications in computer networks, bioinformatics, music and games.

Introduction to the special session:

Evolutionary computation (EC) techniques, including genetic algorithm, evolution strategies, genetic programming, particle swarm optimization, ant colony optimization, differential evolution, and memetic algorithms, have shown to be effective for search and optimization problems. Recently, EC gained several promising results and becomes an important tool in computational creativity, such as in music, visual art, literature, architecture, and industrial design.

The aim of this special session is to reflect the most recent advances of EC for Music, Art, and Creativity, with the goal to enhance autonomous creative systems as well as human creativity. This session will allow researchers to share experiences and present their new ways for taking advantage of EC techniques in computational creativity. Topics of interest include, but are not limited to, EC technologies in the following aspects:

• Generation of music, visual art, literature, architecture, and industrial design
• Algorithmic design in creative intelligence
• Optimization in creativity
• Development of hardware and software for creative systems
• Evaluation methodologies
• Assistance of human creativity
• Computational aesthetics
• Emotion response
• Human-machine creativity

Keywords

Evolutionary computation, computational creativity, music, visual art, creative intelligence, emotion response, and aesthetics

Call for Papers CEC2015 Special Session "When Evolutionary Computation Meets Data Mining"

Special Session for IEEE CEC 2015.
 

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   

Co-Chairs

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 the Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan. His research interests are in evolutionary computation, computational intelligence, metaheuristic algorithms, and their applications in computer networks, data mining, bioinformatics, music and games.

Jun 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 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.

Call for Papers CEC2015 Special Session "Intelligent Network Systems"

Aim and Scope:

The impact of optimization in network environments, such as communication networks and transportation networks, on the modern economy and society has been growing steadily over the last few decades. The worldwide division of labor, the connection of distributed centers, and the increased mobility of individuals and devices lead to an increased demand for efficient solutions to solve optimization problems in network systems. With the advent of computer systems, computational intelligence approaches have been developed for systematic design, optimization, and improvement of different network systems.

The aim of the special session is to promote research and reflect the most recent advances of computational intelligence, including evolutionary computation, neural network, fuzzy systems, metaheuristic techniques and other intelligent methods, in the solution of problems in network systems.

Topics of interest include, but are not limited to:

• Communication network systems: telecommunications; mobile, satellite, optical, and voice communications; personal communication systems; switching and routing; transmission systems; communication systems simulation; station and antenna design; information and speech processing; intrusion detection; error control coding; compression and cryptography; propagation and channel modeling, protocol design, etc.
• Transportation and logistics network systems: transportation and supply networks; logistics; supply chain management; freight and passenger services; tracking and tracing; fleet and order management; modeling and traffic management; traffic simulation; individual and public transportation; inventory optimization; routing and scheduling, etc.
• Social network systems: action policies; networking strategies; network and friendship management; identification of interests; advertisement of interests; hierarchical networks; distributed games; behavior analysis; inter-personal communication; group communication, etc.
• Financial and economic network systems: system modeling; modeling payment system, market modeling; forecasting market prices; price tracking; invest strategies; portfolio strategies; measuring systemic importance of the financial system though the network topology, etc.
• General network problems: parallel and distributed systems; networks and graph problems; unconstrained and constrained network design problems; structural and computational complexity; adaptability to environmental variations; robustness to network changes and failures; effectiveness and scalability of performance; location and link design; reliability and failure; corporate network design; location placement; network physical and software architecture; network hardware and software technologies; operations, maintenance, and management; signaling and control; active networks; network services and applications, etc.

 

Organizers:

This special session is organized by IEEE CIS ISATC Task Force on Intelligent Network Systems (TF-INS).

Hui Cheng   

Senior Lecturer, Liverpool John Moores University, UK, Email: H.Cheng@ljmu.ac.uk   
Hui Cheng received the BSc and the MSc degrees in Computer Science from Northeastern University, China in 2001 and 2004, and the Ph.D degree in Computer Science from The Hong Kong Polytechnic University, Hong Kong in 2007. From January 2008 to July 2010, he was employed as a Research Associate at University of Leicester, UK. He joined Department of Computer Science and Technology at University of Bedfordshire as a Lecturer in October 2010. He is currently a Senior Lecturer in Liverpool John Moores University. His research interests include artificial intelligence, dynamic optimization, cloud computing, optical networks, mobile ad hoc networks, and QoS routing.

Shengxiang Yang   

Professor, Director of Centre for Computational Intelligence, De Montfort University, UK, Email: syang@dmu.ac.uk   
Shengxiang Yang (M'00-SM'14) received the PhD degree in systems engineering from Northeastern University, China, in 1999. From October 1999 to June 2012, he worked as a Post-Doctoral Research Associate, a Lecturer, and a Senior Lecturer with King's College London, University of Leicester, and Brunel University, respectively. He joined De Montfort University, UK, as a Professor in Computational Intelligence in July 2012. He is now Director of the Centre for Computational Intelligence, School of Computer Science and Informatics, De Montfort University.  He has over 180 publications. His current research interests include evolutionary computation (EC), swarm intelligence, meta-heuristics, artificial neural networks, evolutionary multi-objective optimization, computational intelligence in dynamic and uncertain environments, and relevant real-world applications. He is the Chair of IEEE Computational Intelligence Society (CIS) ECTC Task Force on EC in Dynamic and Uncertain Environments, and the Founding Chair of IEEE CIS ISATC Task Force on Intelligent Network Systems (TF-INS). He has given invited keynote speeches in several international conferences and co-organized over 20 workshops and special sessions in conferences. He was the founding Co-Chair of the IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments. He serves as an Area Editor, an Associate Editor, or an Editorial Board Member for four international journals. He has co-edited several books and conference proceedings and co-guest-edited several journal special issues.

Chuan-Kang Ting   

Associate Professor, National Chung Cheng University, Taiwan, Email: 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 communication and transportation networks, bioinformatics, music and games. He is an associate editor of IEEE Computational Intelligence Magazine and an editorial board member of Soft Computing and Memetic Computing journals. He chaired the AI Forum 2012 and co-chaired the 2013 IEEE Symposium on Computational Intelligence for Creativity and Affective Computing.

Call for Papers IJCNN 2015 Special Session "Emerging trends in CI methods for Biomedicine and Healthcare"

Special Session for IEEE IJCNN 2015.


http://www.cs.upc.edu/~avellido/research/conferences/IJCNN15-HealthBiomed.html

Beyond basic research, biology in general and biomedicine in particular are increasingly and rapidly becoming data-based sciences, an evolution driven by technological advances in image and signal non-invasive data acquisition (perfectly exemplified by the 2014 Nobel Prize in Chemistry for the development of super-resolved fluorescence microscopy), or high-throughput genomics, to name just a few. In the Biomedical field, the large amount of data generated from a wide range of devices and patients is creating challenging scenarios for researchers, related to storing, processing and even just transferring information in its electronic form, all these compounded by privacy and anonymity legal issues. The situation is not different in healthcare, where electronic health records are becoming commonplace and new possibilities such as remote home monitoring, or wearable medical devices are likely to make an impact as part as the ambitious p-Health, or 4-P (Predictive, personalized, preventive, participatory) paradigm for medicine. Data-based healthcare finds a paramount example in the current Institute for Systems Biology (ISB, Seattle) "Hundred Person Wellness Project"(*), a pilot in which 100 healthy individuals are intensively monitored on a daily basis. New data requirements require new approaches to data analysis, some of the most interesting ones are currently stemming from the Computational Intelligence (CI) and Machine Learning (ML) community. This session is particularly interested in the proposal of novel CI and ML approaches to problems in the biomedical and healthcare domains, with a non-exclusive focus on methods that overcome the "black-box syndrome" by making models interpretable and thus fulfil the usability requirements of most real medical applications.

Topics that are of interest to this session include (but are not necessarily limited to):

• Novel applications of existing CI and ML methods to biomedicine and healthcare
• Novel CI and ML techniques for biomedicine and healthcare.
• CI and ML-based methods to improve model interpretability in biomedicalproblems, including data/model visualization techniques.
• Novel CI and ML techniques for dealing with non-structured and heterogeneousdata formats.
• Development of user-friendly interactive exploratory interfaces and subjectspecificmodels.

(*) Medicine Gets Up Close and Personal, Nature, 506(7487), 144-145

Organizers:

• Alfredo Vellido, Computer Science Department, Universitat Politècnica de Catalunya BarcelonaTECH, Spain
• Paulo J.G. Lisboa and Sandra Ortega-Martorell, School of Computing and Mathematical Sciences, Liverpool John Moores University, United Kingdom
• José D. Martín, Intelligent Data Analysis Laboratory (IDAL), Department of Electronic Engineering, University of Valencia, Spain

CEC 2015 Special Session and Competition on: "Niching Methods for Multimodal Optimization"

Special Session for IEEE CEC 2015.


URL: http://goanna.cs.rmit.edu.au/~xiaodong/cec15-niching/
URL: http://goanna.cs.rmit.edu.au/~xiaodong/cec15-niching/competition/

Objectives

Population based meta-heuristic algorithms such as Evolutionary Algorithms (EAs) in their original forms are usually designed for locating a single global solution. These algorithms typically converge to a single solution because of the global selection scheme used. Nevertheless, many real-world problems are "multimodal" by nature, i.e., multiple satisfactory solutions exist. It may be desirable to locate many such satisfactory solutions so that a decision maker can choose one that is most proper in his/her problem domain. Numerous techniques have been developed in the past for locating multiple optima (global or local). These techniques are commonly referred to as "niching" methods. A niching method can be incorporated into a standard EA to promote and maintain formation of multiple stable subpopulations within a single population, with an aim to locate multiple globally optimal or suboptimal solutions. Many niching methods have been developed in the past, including crowding, fitness sharing, derating, restricted tournament selection, clearing, speciation, etc. In more recent times, niching methods have also been developed for other meta-heuristic algorithms such as Particle Swarm Optimization and Differential Evolution.

Most of existing niching methods, however, have difficulties that need to be overcome before they can be applied successfully to real-world multimodal problems. Some identified issues include: difficulties to pre-specify some niching parameters; difficulties in maintaining found solutions in a run; extra computational overhead; poor scalability when dimensionality and modality are high. This special session aims to highlight the latest developments in niching methods, bringing together researchers from academia and industries, and exploring future research directions on this topic. We invite authors to submit original and unpublished work on niching methods. Topics of interest include but are not limited to:

• Theoretical developments in multimodal optimization
• Niching methods that incurs lower computational costs
• Handling the issue of niching parameters in niching methods
• Handling the scalability issue in niching methods
• Handling problems characterized by massive multi-modality
• Adaptive or parameter-less niching methods
• Multiobjective approaches to niching
• Multimodal optimization in dynamic environments
• Niching methods applied to discrete multimodal optimization problems
• Niching methods applied to constrained multimodal optimization problems
• Niching methods using parallel or distributed computing techniques
• Benchmarking niching methods, including test problem design and performance metrics
• Comparative studies of various niching methods
• Niching methods applied to engineering and other real-world multimodal optimization problems

Please note that we are NOT interested if the adopted task is to find a single solution of a multimodal problem.

Furthermore, a companion competition on Niching Methods for Multimodal Optimization will also be organized in conjunction with our special session. See further information at:

http://goanna.cs.rmit.edu.au/~xiaodong/cec15-niching/competition/

The aim of the competition is to provide a common platform that encourages fair and easy comparisons across different niching algorithms. The competition allows participants to run their own niching algorithms on 20 benchmark multimodal functions with different characteristics and levels of difficulty. Researchers are welcome to evaluate their niching algorithms using this benchmark suite, and report the results by submitting a paper to the associated niching special session (i.e., submitting via the online submission system of CEC'2015). In case it is too late to submit the paper (i.e., passing the CEC'2015 submission deadline), author may submit their results in a report directly to the special session organizers, in order to be considered in the competition.

Important Dates

• Paper Submission: 19 December 2014
• Notification of Acceptance: 20 February 2015
• Final Paper submission: 13 March 2015

Paper Submission

Manuscripts should be prepared according to the standard format and page limit specified in CEC'2015. For more submission instructions, please see the CEC'2015 submission page at: http://sites.ieee.org/cec2015/

Please indicate during submission that your paper is submitted to this special session.

Technical Committee

Michael N. Vrahatis, University of Patras, Greece
Patrick  Siarry, Universite Paris-Est Creteil Val-de-Marne, France
Jian-Ping Li, Bradford University, UK
Mike Preuss, Dortmund University, Germany
Konstantinos E. Parsopoulos, University of Ioannina, Greece
Jonathan Mwaura, University of Pretoria, South Africa
Ofer M. Shir, Tel-Hai College and MIGAL Institute, Israel
Nicos Pavlidis, Lancaster University, UK
Bo-Yang Qu, Zhenzhou University, China

Special Session Organizers

Xiaodong Li, RMIT University, Australia
Andries Engelbrecht, University of Pretoria, South Africa
Michael G. Epitropakis, University of Stirling, Scotland

Call for Papers CEC2015 Special Session "Complex Adaptive Systems"

Special Session for IEEE CEC 2015.

Aim and Scope

Complex adaptive systems involve many components, often called agents that interact and adapt or learn. It is a system composed of many interacting agents, such that the collective behavior of those agents together is more than the sum of their individual behaviors. The collective behaviors are sometimes also called emergent behaviors, and a complex adaptive system can thus be said to be a system of interacting parts that displays emergent behavior. Examples include ecosystems, stock markets and economies, biological evolution, and indeed the whole of human society.
This special track seeks high-quality original and unpublished papers in the following topics including but not limited to these topics:

• Adaptation
• Agent-based modeling
• Biological evolution
• Cellular automata
• Collective action
• Criticality
• Dynamical systems
• Economics and market
• Ecosystem emergence
• Evolutionary system
• Game theory
• Human societies
• Immune system
• Information theory
• Intelligent system
• Pattern formation
• Risk management system
• Social network
• Socio-physics
• Synchronization

Organizers

Hiroshi SATO, hsato@nda.ac.jp, National Defense Academy, Japan
Hiroshi Sato is Associate Professor of Department of Computer Science at National Defense Academy in Japan. He received B.E. degree in Physics from Keio University in Japan, and M.E and D.E in Computer Science from Tokyo Institute of Technology in Japan. His research interests include agent-based simulation, evolutionary computation, and artificial intelligence. He is a member of Japanese Society for Artificial Intelligence, Society of Instrument and Control Engineers and The Institute of Electronics, Information and Communication Engineers.

Masao KUBO, masaok@nda.ac.jp, National Defense Academy, Japan
Masao KUBO is an associate Professor of Department of Computer Science at National Defense Academy in Japan. He received B.E. degree in Precision Engineering and M.E and D.E in Computer Science from Hokkaido University in Japan. His research interests include swarm robotics, collective intelligence, and artificial intelligence. He is a member of Japanese Society for Artificial Intelligence, Society of Instrument and Control Engineers and the Robotics Society of Japan.

Saori IWANAGA, s-iwanaga@jcga.ac.jp, Japan Coast Guard Academy, Japan
Saori IWANAGA is Professor of Department of Maritime Safety Technology at Japan Coast Guard Academy (JCGA) since 2012. She received her B.E. degree in applied chemistry engineering from Utsunomiya University, Japan, in 1994 and her M.S. and her Ph.D. degree in computer science from National Defense Academy, Japan, in 2001 and 2004, respectively. She has worked at JCGA since 2007. She is interested in complex theory, evolutionary games. She is a member of Information Processing Society of Japan and Japan Society for Safety Engineering.

Call for Papers CEC2015 Special Session "Combining Evolutionary Computation and Reinforcement Learning"

Special Session for IEEE CEC 2015.

Evolutionary Computation (EC) and Reinforcement Learning (RL) are two research fields in the area of search, optimization, and control.  RL addresses sequential decision making problems in initially unknown stochastic environments, involving stochastic policies and unknown temporal delays between actions and observable effects. EC studies algorithms that can optimize some fitness function by searching for the optimal set of parameter values. RL can quite easily cope with stochastic environments, which is more complex with traditional EC methods. The main strengths of EC techniques are their general applicability to solving many different kinds of optimization problems and their global search behavior enabling these methods not to get easily trapped in local optima. There also exist EC methods that deal with adaptive control problems such as classifier systems and evolutionary reinforcement learning. Such methods address basically the same problem as in RL, i.e. the maximization of the agent's reward in a potentially unknown environment that is not always completely observable. Still, the approach taken by these methods are different and complementary. RL is used for learning the parameters of a single model using a fixed representation of the knowledge and learns to improve its value function from the reward given after every step taken in an environment. EC is usually a population based optimizer that uses a fitness function to rank individuals based on their total performance in the environment and uses different operators to guide the search. These two research fields can benefit from an exchange of ideas resulting in a better theoretical understanding and/or empirical efficiency.

Aim and scope

The main goal of this special session is to solicit research on frontiers and potential synergies between evolutionary computation and reinforcement learning. We encourage submissions describing applications of EC for optimizing agents in difficult environments that are possibly dynamic, uncertain and partially observable, like in games, multi-agent applications such as scheduling, and other real-world applications. Ideally, this special session will gather research papers with a background in either RL or EC that propose new challenges and ideas as result of synergies between RL and EC.

Topics of interests

We enthusiastically solicit papers on relevant topics such as:

• Novel frameworks including both evolutionary algorithms and RL
• Comparisons between RL and EC approaches to optimize the behavior of agents in specific environments
• Parameter optimization of EC methods using RL or vice versa
• Adaptive search operator selection using reinforcement learning
• Optimization algorithms such as meta-heuristics, evolutionary algorithms for dynamic and uncertain environments
• Theoretical results on the learnability in dynamic and uncertain environments
• On-line self-adapting systems or automatic configuration systems
• Solving multi-objective sequential decision making problems with EC/RL
• Learning in multi-agent systems using hybrids between EC and RL
• Learning to play games using optimization techniques
• Real-world applications in engineering, business, computer science, biological sciences, scientific computation, etc. in dynamic and uncertain environments solved with evolutionary algorithms
• Solving dynamic scheduling and planning problems with EC and/or RL

Organizers

Madalina M. Drugan (mdrugan@vub.ac.be)  Artificial Intelligence Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium

Bernard Manderick (Bernard.Manderick@vub.ac.be) Artificial Intelligence Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium

Marco A. Wiering (m.a.wiering@rug.nl) Institute of Artificial Intelligence and Cognitive Engineering, University of Groningen, Nijenborgh 9, 9700AK Groningen, The Netherlands

Call for Papers CEC2015 Special Session "Developmental Swarm Intelligence"

Special Session for IEEE CEC 2015.

Overview

Swarm intelligence algorithm should have two kinds of ability: capability learning and capacity developing. The Capacity Developing focuses on moving the algorithm’s search to the area(s) where higher searching potential may be possessed, while the capability learning focuses on its actually searching from the current solution for single point based optimization algorithms and from the current population for population-based swarm intelligence algorithms.

The capacity developing is a top-level learning or macro-level learning. The capacity developing is the learning ability of an algorithm to adaptively change its parameters, structures, and/or its learning potential according to the search states on the problem to be solved. In other words, the capacity developing is the search strength possessed by an algorithm. The capability learning is a bottom-level learning or micro-level learning. The capability learning is the ability for an algorithm to find better solution(s) from current solution(s) with the learning capacity it is possessing.

The brain storm optimization (BSO) algorithm and Fireworks algorithm (FWA) are two good examples of developmental swarm intelligence (DSI) algorithms. The “good enough” optimum could be obtained through the solutions divergence and convergence in the search space. In BSO algorithm, the solutions are clustered into several categories, and the new solutions are generated by the mutation of cluster or existed solutions. While in FWA algorithm, mimicked by the fireworks exploration, the new solutions are generated by the exploration of existed solutions. The capacity developing, i.e., the adaptation in search, is another common feature in these two algorithms.

Topics of Interest

This special session aims at presenting the latest developments of developmental swarm intelligence algorithms, as well as exchanging new ideas and discussing the future directions of developmental swarm intelligence. Original contributions that provide novel theories, frameworks, and applications to developmental swarm intelligence are very welcome for this Special Session. Potential topics include, but are not limited to:

• Analysis and control of DSI parameters
• Parallelized and distributed realizations of DSI algorithms
• DSI for Multi-objective optimization
• DSI for Constrained optimization
• DSI for Discrete optimization
• DSI in uncertain environments
• Theoretical aspects of DSI algorithm
• DSI for Real-world applications

Submission

Please follow the IEEE CEC2015 instruction for authors and submit your paper via the IEEE CEC 2015 online submission system. Please specify that your paper is for the Special Session on Developmental Swarm Intelligence.

Important Dates

Paper Submission Deadline:          December 19, 2014
Notification of Acceptance:             February 19, 2015
Final Paper Submission Deadline:  March 12, 2015

Organisers

Shi Cheng, University of Nottingham Ningbo, China, shi.cheng-at-nottingham.edu.cn
Quande Qin, Shenzhen University, Shenzhen China, qdqin-at-szu.edu.cn
Yuhui Shi, Xi'an Jiaotong-Liverpool University, Suzhou China, yuhui.shi-at-xjtlu.edu.cn

Biography of the Proposers

Shi Cheng received the Bachelor's degree in Mechanical and Electrical Engineering from Xiamen University, Xiamen, the Master's degree in Software Engineering from Beihang University (BUAA), Beijing, China, the Ph.D. degree in Electrical Engineering and Electronics from Liverpool University, Liverpool, United Kingdom, the Ph.D. degree in Electrical and Electronic Engineering from Xi’an Jiaotong-Liverpool University, Suzhou, China in 2005, 2008, and 2013, respectively. He is currently a research fellow with Division of Computer Science, University of Nottingham Ningbo, China. He has published more than 30 research articles in peer-reviewed journals and international conferences. His current research interests include swarm intelligence, multiobjective optimization, and data mining techniques and their applications.

Quande Qin received PhD degree in Management Science and Engineering from School of Business Administration, South China University of Technology, Guangzhou, China. Currently, he is a lecturer in the College of Management, Shenzhen University, Shenzhen, China. His current research interests include swarm intelligence, evolutionary optimization and their applications in management and economics.

Yuhui Shi received the PhD degree in electronic engineering from Southeast University, Nanjing, China, in 1992. He is a Professor in the Department of Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, China. Before joining Xi'an Jiaotong-Liverpool University, he was with Electronic Data Systems Corporation, Indianapolis, IN, USA. His main research interests include the areas of computational intelligence techniques (including swarm intelligence) and their applications. Dr. Shi is the Editor-in-Chief of the International Journal of Swarm Intelligence Research.

Call for Papers CEC2015 Special Session "Evolutionary Computation in Big Data"

Special Session for IEEE CEC 2015.

Overview

Nowadays, big data has been attracting increasing attention from academia, industry and government. The big data is defined as the dataset whose size is beyond the processing ability of typical database or computers. The big data analytics is to automatically extract knowledge from large amounts of data. It can be seen as mining or processing of massive data, and “useful” information could be retrieved from large dataset. Big data analytics can be characterized with several properties, such as large volume, variety of different sources, and fast increasing speed (velocity). It is of great interest to investigate the role of evolutionary computing (EC) techniques, including evolutionary algorithms and swarm intelligence algorithms in optimization and learning involving big data, in particular the ability of EC techniques to solve large scale, dynamic, and sometimes multi-objective big data analytics problems.

Topics of Interest

1. This special session aims at presenting the latest developments of EC techniques for big data problems, as well as exchanging new ideas and discussing the future directions of EC for big data. Original contributions that provide novel theories, frameworks, and solutions to challenging problems of big data analytics are very welcome for this Special Session. Potential topics include, but are not limited to:High-dimensional and many-objective evolutionary optimization
2. Big data driven optimization of complex engineering systems
3. Integrative analytics of diverse, structured and unstructured data
4. Extracting new understanding from real-time, distributed, diverse and large-scale data resources
5. Big data visualization and visual data analytics
6. Scalable, incremental learning and understanding of big data
7. Scalable learning techniques for big data
8. Big data driven optimization of complex systems
9. Human-computer interaction and collaboration in big data
10. Big data and cloud computing
11. Cross-connections of big data analysis and hardware
12. Big data techniques for business intelligence, finance, healthcare, bioinformatics, intelligent transportation, smart city, smart sensor networks, cyber security and other critical application areas

Submission

Please follow the IEEE CEC2015 instruction for authors and submit your paper via the IEEE CEC 2015 online submission system. Please specify that your paper is for the Special Session on Evolutionary Computation in Big Data.

Important Dates

Paper Submission Deadline:           December 19, 2014
Notification of Acceptance:              February 19, 2015
Final Paper Submission Deadline:  March 12, 2015

Organisers

Shi Cheng, University of Nottingham Ningbo, China, shi.cheng-at-nottingham.edu.cn
Yuhui Shi, Xi'an Jiaotong-Liverpool University, Suzhou China, yuhui.shi-at-xjtlu.edu.cn
Yaochu Jin, University of Surrey, Guildford, United Kingdom, yaochu.jin-at-surrey.ac.uk

Biography of the Proposers

Shi Cheng received the Bachelor's degree in Mechanical and Electrical Engineering from Xiamen University, Xiamen, the Master's degree in Software Engineering from Beihang University (BUAA), Beijing, China, the Ph.D. degree in Electrical Engineering and Electronics from Liverpool University, Liverpool, United Kingdom, the Ph.D. degree in Electrical and Electronic Engineering from Xi’an Jiaotong-Liverpool University, Suzhou, China in 2005, 2008, and 2013, respectively. He is currently a research fellow with Division of Computer Science, University of Nottingham Ningbo, China. He has published more than 30 research articles in peer-reviewed journals and international conferences. His current research interests include swarm intelligence, multiobjective optimization, and data mining techniques and their applications.

Yuhui Shi received the PhD degree in electronic engineering from Southeast University, Nanjing, China, in 1992. He is a Professor in the Department of Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, China. Before joining Xi'an Jiaotong-Liverpool University, he was with Electronic Data Systems Corporation, Indianapolis, IN, USA. His main research interests include the areas of computational intelligence techniques (including swarm intelligence) and their applications. Dr. Shi is the Editor-in-Chief of the International Journal of Swarm Intelligence Research.

Yaochu Jin is currently a Professor of Computational Intelligence with the Department of Computing, University of Surrey, Guildford, U.K., where he heads the Nature Inspired Computing and Engineering Group. He is also a Finland Distinguished Professor awarded by Academy of Finland. His main research interests include computational intelligence, computational neuroscience and computational systems biology, with applications to complex engineering optimization, bioengineering, swarm robotics, and autonomous systems. His current research is funded by EU FP7, UK EPSRC and industries, including Intellas UK, Santander, Aero Optimal, Bosch UK and Honda. He has delivered 20 invited keynote speeches at international conferences.

Dr Jin is the founding chair of the IEEE Symposium on Computational Intelligence in Big Data and Guest Editor of the IEEE Computational Intelligence Magazine special issue on Big Data. He is an Associate Editor of several international journals including IEEE TRANSACTIONS ON CYBERNETICS, IEEE TRANSACTIONS ON NANOBIOSCIENCE, and IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE and BioSystems.

He is currently Vice President for Technical Activities, and IEEE Distinguished Lecturer. He was the recipient of the Best Paper Award of the 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. He is a Fellow of BCS and Senior Member of IEEE.

Call for Papers CEC2015 Special Session "Large Scale Global Optimization"

Special Session for IEEE CEC 2015.

 In the past two decades, many nature-inspired optimization algorithms have been developed and applied successfully for solving a wide range of optimization problems, including Simulated Annealing (SA), Evolutionary Algorithms (EAs), Differential Evolution (DE), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Estimation of Distribution Algorithms (EDA), etc. Although these techniques have shown excellent search capabilities when applying to small or medium sized problems, they still encounter serious challenges when applying to large scale problems, i.e., problems with several hundreds to thousands of variables. The reasons appear to be two-fold.  Firstly, the complexity of a problem usually increases with the increasing number of decision variables, constraints, or objectives (for multi-objective optimization problems).  Problems with this high level of complexity may prevent a previously successful search strategy from locating the optimal solutions.  Secondly, as the size of the solution space of the problem grows exponentially with the increasing number of decision variables, there is an urgent need to develop more effective and efficient search strategies to better explore this vast solution space with only limited computational budgets.

In recent years, researches on scaling up EAs to large scale problems have attracted much attention, including both theoretical and practical studies. Existing work on this topic are still rather limited, given the significance of the scalability issue. This special session is devoted to highlight the recent advances in EAs for handling large scale global optimization (LSGO) problems, involving single objective or multiple objectives, unconstrained or constrained, binary/discrete or real, or mixed decision variables. More specifically, we encourage interested researchers to submit their original and unpublished work on:

• Theoretical and experimental analysis of the scalability of EAs;
• Novel approaches and algorithms for scaling up EAs to large scale optimization problems;
• Applications of EAs to real-world large scale optimization problems;
• Novel test suites that help us understand large scale optimization problem characteristics.

Furthermore, a companion competition on Large Scale Global Optimization (LSGO) will also be organized in conjunction with our special session. The competition allows participants to run their own algorithms on 20 benchmark functions, each of which is of 1000 dimensions. Detailed information about these benchmark functions is provided in the following technical report:

X. Li, K. Tang, M. Omidvar, Z. Yang and K. Qin, "Benchmark Functions for the CEC'2013 Special Session and Competition on Large Scale Global Optimization," Technical Report, Evolutionary Computation and Machine Learning Group, RMIT University, Australia, 2013

The aim of this competition is to provide a common platform that encourages fair and easy comparisons across different LSGO algorithms. Researchers are welcome to apply any kind of evolutionary computation technique to the test suite. The technique and the results can be reported in a paper for the special session (i.e., submitted via the online submission system of CEC’2015).

Paper Submission

Manuscripts should be prepared according to the standard format and page limit of regular papers specified in CEC’2015 and submitted through the CEC’2015 website: http://www.cec2015.org. Special session papers will be treated in the same way as regular papers and included in the conference proceedings.

Special Session Organizers

Professor Ke Tang
The USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI)
School of Computer Science and Technology
University of Science and Technology of China, Hefei, Anhui, China
Email: ketang@ustc.edu.cn, Website: http://staff.ustc.edu.cn/~ketang

Associate Professor Xiaodong Li
School of Computer Science and Information Technology,
RMIT University, Australia
Email: xiaodong.li@rmit.edu.au, Website: http://goanna.cs.rmit.edu.au/~xiaodong/

Dr. Zhenyu Yang
College of Information System and Management
National University of Defense Technology (NUDT), Changsha, China
Email: zhyuyang@ieee.org

Associate Professor Daniel Molina
School of Engineering
University of Cádiz, Spain
Email: daniel.molina@uca.es

Organizer Bios:

Ke Tang received the B.Eng. degree from the Huazhong University of Science and Technology, Wuhan, China, and the Ph.D. degree from the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, respectively. He is now a professor at the USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, Hefei, China. He has authored or co-authored more than 70 refereed papers in journals and conferences. His research interests include evolutionary computation, machine learning, and real-world applications. He is an Associate Editor of the IEEE Computational Intelligence Magazine and Computational Optimization and Applications. He served as the Program/Technical Co-Chair of CEC2010 and CEC2013.

Xiaodong Li received his B.Sc. degree from Xidian University, Xi'an, China, and Ph.D. degree in information science from University of Otago, Dunedin, New Zealand, respectively. Currently, he is an Associate Professor at the School of Computer Science and Information Technology, RMIT University, Melbourne, Australia. His research interests include evolutionary computation, machine learning, complex systems, multiobjective optimization, and swarm intelligence. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation and International Journal of Swarm Intelligence Research. He is a founding member and currently a Vice-chair of IEEE CIS Task Force on Swarm Intelligence, and currently a Chair of IEEE CIS Task Force on Large Scale Global Optimization.  He was the General Chair of SEAL'08, a Program Co-Chair AI'09, and a Program Co-Chair for IEEE CEC’2012.  He is the recipient of 2013 SIGEVO Impact Award.

Zhenyu Yang received the B.Eng. and Ph.D. degrees from the University of Science and Technology of China, Hefei, China, in 2005 and 2010, both in computer science. He is currently a Lecturer at the College of Information Systems and Management, National University of Defense Technology, Changsha, China. His research interests include metaheuristics, such as evolutionary algorithms for global optimization, large-scale optimization, and various real-world applications. He is a founding member of the IEEE CIS Task Force on Large Scale Global Optimization. He serves as an Associate Editor of Computational Optimization and Applications.

Daniel Molina received the B.Eng. Degree from the University of Granada, Spain, and the Ph.D. from the same University at 2007. He is currently a Lecturer at the School of Engineering, University of Cadiz. He has authored or co-authored more than 40 refereed papers in journals and conferences. His research interests include metaheuristics, such as evolutionary algorithms for global optimization, niching optimization, large-scale optimization, and real-world applications. He is a member of the IEEE CIS Task Force on Large Scale Global Optimization.
   

Call for Papers CEC2015 Special Session "Evolutionary Exploration of Design Spaces"

Special Session for IEEE CEC 2015.

AIM

The aim of this special session is to serve scientists and practitioners, who are dealing with Design Space Exploration (DSE), to exchange ideas about DSE, and about methods to overcome the computational challenge that is inherent to it.

SCOPE

The scope of this special session includes:

• Evolutionary and hybrid search algorithms for DSE
• Multi-objective  and many-objective DSE (multiobjectivization)
• Multi-scenario-based DSE
• Novelty based DSE
• Problem formulation and re-formulation
• Evolving design scenarios
• Subspaces as design concepts (conceptual solutions)
• Preference of sub-spaces and conceptual designs (concepts)
• Design-space representations and automated concept generation
• Mixed methods from Machine Learning, EC, and Multi-criteria Decision Making
• Knowledge discovery and knowledge confirmation
• Extraction of design rules and innovization
• Extraction of creative conceptual and particular solutions
• In-process visualization and signaling
• Interactive setting of search parameters
• Handling of multi-modality in DSE
• DSE with uncertainties
• DSE and the Toyota 2nd Paradox (set-based concurrent engineering)
• Complexity and efficiency
• Surrogate models for DSE
• Parallel processing for DSE
• Taxonomy and characteristics of DSE systems
• Demonstration of DSE with real-life problems
• DSE for educating novice engineers
• Model validation with DSE
• Any other relevant topic

IMPORTANT DATES

Paper Submission Deadline: December 19, 2014
Paper Acceptance Notification: February 20, 2015
Final Paper Submission Deadline: March 13, 2015
Early Registration: March 13, 2015
Conference Dates: May 25-28, 2015

SUBMISSION

Please submit your paper via  online submission system.
Please follow the IEEE CEC2015 instruction for authors. 
Please specify that your paper is for the SS54 on Evolutionary Exploration of Design Spaces.

ORGANIZERS

Amiram (Ami) Moshaiov, Tel-Aviv University,  moshaiov (at) eng.tau.ac.il
Kalyanmoy Deb, Michigan State University, 

ADDITIONAL INFORMATION

For more details and up-to-date news, please visit our workshop web site at

    http://www.eng.tau.ac.il/~moshaiov/ieee-cec-2015

Call for Papers CEC2015 Special Session "Unbounded Real-Parameter Blackbox Optimization"

Special Session for IEEE CEC 2015.

Benchmarking of optimization algorithms is crucial to assess performance of optimizers quantitatively, understand weaknesses and strengths of each algorithm and is the compulsory path to test new algorithm designs.

A thorough benchmarking methodology has been defined in [1] and is implemented within the COCO framework (http://coco.gforge.inria.fr/) that furnishes most of the tedious tasks of benchmarking for the participants:

• choice of well-motivated single-objective benchmark functions and their implementation in Matlab, C, Java, R, and Python,
• design of an experimental set-up,
• generation of data output, and
• post-processing and presentation of the results in graphs and tables (up to already prepared LaTeX templates for writing papers).

Note that for the CEC-BBOB-2015 special session, we provide essentially the same test-suite as in the previous editions of BBOB held at GECCO.
We provide 2 testbeds,

• noise-free and
• noisy,

and we distinguish between

• an expensive optimization scenario (where a focus on the first 100D function evaluations is assumed) and
• a general scenario for which we do not limit the maximal number of function evaluations made.

The participants can freely choose any or all of them. We entirely ban different parameter settings for different test functions and encourage analyses that study the impact of parameter setting changes.

[1] Real-Parameter Black-Box Optimization Benchmarking: Experimental Setup,
http://coco.lri.fr/downloads/download13.09/bbobdocexperiment.pdf.

The Special Session Papers

We encourage any submission that is concerned with black-box optimization benchmarking of continuous optimizers, for example papers that:

• decribe and benchmark new or not-so-new algorithms on the CEC-BBOB-2015 testbed,
• compare new or existing algorithms from the COCO database, or 
• analyze the data obtained in previous editions of BBOB.

Similarly to the BBOB-2013 edition, we also focus on benchmarking optimization algorithms for expensive optimization (with limited budget) which especially invites to benchmark surrogate-assisted algorithms (e.g. based on kriging, support vector machines etc.). Participants are also encouraged to use the existing database for statistical analyses or for designing a portfolio of algorithms.

This session is related to the special session and competition on bound constrained optimization organized by Ponnuthurai Nagaratnam Suganthan et al. (http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC2015/CEC2015.htm). Submissions to both sessions are encouraged while we require here that papers presenting benchmarking results follow the benchmarking methodology of the COCO framework. Papers discussing benchmarking methodologies in general are also welcome.

Organisation of the Special Session during the CEC conference

During the special session, algorithms and results will be presented by the participants. An overall analysis and comparison will be accomplished by the organizers and all submitted papers will be critically reviewed as for any other CEC'2015 paper. A planned plenary discussion on future improvements will, among others, address the question, of how the testbed should evolve.

Support Material and Downloads

All the support material needed to benchmark your algorithm(s), process the results, and compile the final papers will be available at the CEC-BBOB-2015 page (http://coco.gforge.inria.fr/doku.php?id=cec-bbob-2015).

The final code and LaTeX templates following the CEC'2015 guidelines will be available in November. To ensure/check that your optimizer and the COCO framework work well together, the currently available downloads (for past BBOB workshops) can be used. The code for the final experiments and post-processing obey the very same interface. Subscribe for special session announcements (see below) to be notified on updates.

Important dates

01/11/2014: code released
19/12/2014: submission deadline20/02/2015: acceptance notification
13/03/2015: final paper version due
25-28/05/2015: workshop at CEC'2015

Contact and Mailing List

Subscribe to our discussion mailing list by following this link: http://lists.lri.fr/cgi-bin/mailman/listinfo/bbob-discuss.

To receive announcement about the workshop, send an email to the BBOB team at bbob_at_lri.fr with title "register to BBOB announcement list".

Organization Committee

Youhei Akimoto, Shinshu University, Nagano, Japan
Anne Auger, Inria Saclay - Ile-de-France, Orsay, France
Dimo Brockhoff, Inria Lille - Nord Europe, Villeneuve d'Ascq, France
Nikolaus Hansen, Inria Saclay - Ile-de-France, Orsay, France
Olaf Mersmann, TU Dortmund University, Dortmund, Germany
Petr Posik, Czech Technical University, Prague, Czech Republic

Call for Papers CEC2015 Special Session "Evolutionary Computation for Cognitive Robotics"

Special Session for IEEE CEC 2015.

Recently, various types of intelligent robots have been developed for the society of the next generation. In particular, intelligent robots should continue to perform tasks in real environments such as houses, commercial facilities and public facilities. The growing need to automate daily tasks combined with new robot technologies are driving the development of human-friendly robots, i.e., safe and dependable machines, operating in the close vicinity to humans or directly interacting with persons in a wide range of domains. The technology shift from classical industrial robots, which are safely kept away from humans in cages, to robots, which will be used in close collaboration with humans, requires major technological challenges that need to be overcome. Computational intelligence is very important to provide human-friendly services by robots. A robot should have human-like intelligence and cognitive capabilities to co-exist with people. The study on the intelligence, cognition, and self of robots has a long history. The concepts on adaptation, learning, and cognitive development should be introduced more intensively in the next generation robotics from the theoretical point of view. Fuzzy, neural, and evolutionary computation play important role to realize cognitive development of robots from the methodological point of view. Furthermore, the synthesis of information technology, network technology, and robot technology may bring the brand-new emerging intelligence to robots from the technical point of view. The structurization of information and knowledge is a key topic to support the cognitive development of robots. This special session focuses on the intelligence of robots emerging from the adaptation, learning, and cognitive development through the interaction with people and dynamic environments from the conceptual, theoretical, methodological, and/or technical points of view. 

The topics of interests in the special session include, but are not limited to:

• Robot Intelligence   
• Learning, Adaptation, and Evolution in Robotics
• Human-Robot Interaction    
• Embodied Cognitive Science    
• Perception and Action   
• Intelligent Robots 
• Fuzzy, Neural, and Evolutionary Computation for Robotics 
• Evolutionary Robotics
• Soft Computing for Vision and Learning
• Informationally Structured Space.

Important dates:

Paper Submission: 19 December 2014
Paper acceptance notification: 20 February 2015
Final paper submission deadline: 13 March 2015

Organizers:

Janos Botzheim, Graduate School of System Design, Tokyo Metropolitan University, Japan
botzheim@tmu.ac.jp

Janos Botzheim was born in Budapest, Hungary, in 1978. He earned the M.Sc. and Ph.D. degrees in Computer Science at the Budapest University of Technology and Economics in 2001 and 2008, respectively. He is an associate professor in the Graduate School of System Design at the Tokyo Metropolitan University. He is a member of several scientific societies such as John von Neumann Computer Science Society, Hungarian Fuzzy Association, IEEE. His research interests are: computational intelligence, especially evolutionary and memetic algorithms; computational intelligence applications in robotics. He is the Symposium Chair of the IEEE Symposium on Robotic Intelligence in Informationally Structured Space (RiiSS) 2014.


Chu Kiong Loo, Faculty of Computer Science and Information Technology University of Malaya, Malaysia ckloo.um@um.edu.my

Chu Kiong Loo obtained his PhD (University Sains Malaysia), B.Eng (First class Hons in Mechanical Engineering from University Malaya). Formerly he was a design engineer in various industrial firms in different capacities as well as he has been the chairman of Centre for Robotics and Automation in Multimedia University. Currently he is a professor in Artificial Intelligence Department, Faculty of Computer Science and Information Technology, University of Malaya, Malaysia.
He has published many publications in peer-reviewed journals of robotics, artificial intelligence and soft-computing, quantum optics that are recognized as outstanding and appropriate to the discipline: Based on the theoretical foundation of Prof. Karl H. Pribram in Holonomic Brain Theory he continues the co-development of Dendritic field network with Dr. Mitja Perus and their major work is published in the scientific book, “Biological and Quantum Computing for Human Vision: Holonomic Models and Applications”, 2011. He is the Symposium Chair of the IEEE Symposium on Robotic Intelligence in Informationally Structured Space (RiiSS) 2014.


Naoyuki Kubota, Graduate School of System Design, Tokyo Metropolitan University, Japan
kubota@tmu.ac.jp

Naoyuki Kubota received the B.Sc. degree from Osaka Kyoiku University, Kashiwara, Japan, in 1992, the M.Eng. degree from Hokkaido University, Hokkaido, Japan, in 1994, and the D.E. degree from Nagoya University, Nagoya, Japan, in 1997. He joined the Osaka Institute of Technology, Osaka, Japan, in 1997. In 2000, he joined the Department of Human and Artificial Intelligence Systems, Fukui University, as an Associate Professor. He joined the Department of Mechanical Engineering, Tokyo Metropolitan University, in 2004. He is a Professor with the Department of System Design, Tokyo Metropolitan University, Tokyo, Japan. He was the Symposium Chair of the IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS) in 2009, 2011, and 2013.