CFP: IEEE CEC 2019 Special Session on Pigeon-Inspired Optimization

Swarm intelligence algorithm should have two kinds of ability: capability learning and capacity developing. The Pigeon-Inspired Optimization (PIO) algorithm is a new kind of swarm intelligence, which is based on the behaviors of homing pigeons. It is natural to expect that an optimization algorithm based on pigeons could be a better optimization algorithm than existing swarm intelligence algorithms which are based on collective behavior of simple insects, because pigeons have strong individual and social ability. The designed optimization algorithm will naturally have the capability of both convergence and divergence.

The PIO algorithm is a good example of developmental swarm intelligence algorithm. A “good enough” optimum could be obtained through solution divergence and convergence in the search space. In the PIO algorithm, the process of optimization is considered to be the homing of pigeons. the homing pigeons can easily find their home with the aid of three homing tools: the magnetic field, the sun and the landmarks. Pigeons rely more on map and compass-like information at the beginning of the journey. Landmarks provide more information to pigeons in the midway. Moreover, the route is evaluated and revised timely to guarantee that they can reach the destination through the optimal route. Inspired by these facts, two operators are introduced in the PIO algorithm, i.e., the map and compass operator and the landmark operator.

The PIO algorithm is a combination of swarm intelligence and data mining techniques. Every pigeon optimization algorithm is not a solution to the problem to be optimized, but also a data point to reveal the landscapes of the problem. The swarm intelligence and data mining techniques can be combined to produce benefits above and beyond what either method could achieve alone.

Aim and Scope

This special session aims at presenting the latest developments of PIO algorithm, 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 algorithms are very welcome for this Special Session.

Authors are invited to submit their original and unpublished work to this special session. Topics of interest include but are not limited to:

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

Important dates

• Paper submission: 7 January, 2019
• Decision notification: 7 March, 2019
• Camera ready paper due: 31 March, 2019
• Registration: 31 March, 2019
• Conference: 10 June, 2019

Note: all deadlines are 11:59pm US pacific time.

Paper Submission

Please follow the IEEE CEC2019 Submission Web Site. Special session papers are treated the same as regular conference papers. Please specify that your paper is submitted to Pigeon-Inspired Optimization (PIO). All papers accepted and presented at CEC2019 will be included in the conference proceedings published by IEEE Explore, which are typically indexed by EI.

Organisers

Haibin Duan - School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, China.
hbduan@buaa.edu.cn
Phone: +86-10-8231-7318; Fax: +86-10- 8232-8116.

Yin WANG - College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
yinwangee@nuaa.edu.cn
Phone: +86-25-8489-2805.

CFP: IEEE CEC 2019 Special Session on Evolutionary Computation in Healthcare and Biomedical Data

Healthcare and biomedical sciences have become data-intensive fields, with a strong need for sophisticated data mining methods to extract the knowledge from the available information. For example, data analysis methods are applied on biomedical datasets, namely DNA microarray data or Next Gen sequencing data to predict treatment outcomes of paediatric Acute Lymphoblastic Leukaemia patients. Moreover, clustering methods are routinely used to investigate the interpretation of the correlated genes associated with cellular and biological function.

Biomedical data contains several challenges in data analysis, including high dimensionality, class imbalance and low numbers of samples. Although the current research in this field has shown promising results, several research issues need to be explored as follows. There is a need to explore feature selection methods to select stable sets of genes to improve predictive performance along with interpretation. There is also a need to explore big data in biomedical and healthcare research. An increasing flood of data characterises human health care and biomedical research. Healthcare data are available in different formats, including numeric, textual reports, signals and images, and the data are available from different sources. An interesting aspect is to integrate different data sources in the data analysis process which requires exploiting the existing domain knowledge from available sources. The data sources can be ontologies, annotation repositories, and domain experts’ reports.

This special session aims to bring together the current research progress (from both academia and industry) on data analysis for biomedical and healthcare applications. It will attract healthcare practitioners who have access to interesting sources of data but lack the expertise in using the data mining effectively. Special attention will be devoted to handle feature selection, class imbalance, and data fusion in biomedical and healthcare applications.

Topics:

The main topics of this special session include, but are not limited to, the following:

• Information fusion and knowledge transfer in biomedical and healthcare applications.
• Data Analysis of the biomedical data including genomics.
• Text mining for medical reports.
• Statistical analysis and characterization of biomedical data.
• Machine Learning Methods Applied to Medicine
• Large Datasets and Big Data Analytics on biomedical and healthcare applications.
• Information Retrieval of Medical Images
• Single cell sequencing analysis
• Medical imaging and genomics

Paper Submission:

The papers should be submitted through IEEE CEC’s submission central. After logging into the submission system, you need to choose Special Session on “Evolutionary Computation in Healthcare and Biomedical Data”.

Important Dates:

• Paper submission due: Jan. 7, 2019
• Notification of acceptance: Mar. 7, 2019
• Author registration deadline: Mar. 31, 2019
• Camera-ready deadline: Mar. 31, 2019

Information about IEEE CEC 2019: http://cec2019.org/#

Special Session organizers:

• Dr. Mukesh Prasad, University of Technology Sydney, Australia
• Associate Professor Paul J. Kennedy, University of Technology Sydney, Australia,
• Dr. Manoranjan Mohanty, University of Auckland, New Zealand

We look forward to receiving your high-quality submissions!

CFP: IEEE CEC 2019 Special Session on Evolutionary Computation and Neural Network for Combating Cybercrime

The volume of cybercrime is increasing daily with increasing use of the internet for email and social media purposes. The use of neural networks for tackling cybercrime is an active area of research. For example, conventional neural network-based solutions have been proposed to detect image tampering, source camera attribution of an anonymous crime image, explicit content detection, virus detection, etc. Recently, researchers are focusing more on unsupervised solutions. New types of cybercrimes are also emerging (e.g., deepfake video) which may, in turn, require new approaches. Interestingly, the role of evolutionary computing in tackling cybercrime is relatively under explored.

This special session aims to bring together researchers from both academia and industry in the application of evolutionary computation and neural networks for combating cybercrime. This session also will welcome research which focuses on the risk of a neural network for spreading new kind of cybercrimes (e.g., deepfake videos) and evolutionary computing for creating new types of malware (e.g., polymorphic and metamorphic viruses). The session will attract researchers working in cybersecurity, evolutionary computation, and neural networks. Of particular interest will be research that combines evolutionary computing with neural network approaches.

Topics:

The main topics of this special session include, but are not limited to, the following:

• Malware detection using neural networks and evolutionary computation
• Internet fraud detection and prediction using neural networks and evolutionary computation
• Intrusion detection using neural networks and evolutionary computation
• Digital rights management using neural networks and evolutionary computation
• Explicit content filtering using neural networks and evolutionary computation
• Application of convolutional neural networks for multimedia security
• Image and video forensics using convolutional neural networks
• Cybercrime risk due to neural networks (e.g., deepfake)
• Digital forensics for detecting neural network-based fraud

Paper Submission:

The papers should be submitted through IEEE CEC’s submission central. After logging into the submission system, you need to choose Special Session on “Evolutionary Computation and Neural Network for Combating Cybercrime ”.

Important Dates:

• Paper submission due: Jan. 7, 2019
• Notification of acceptance: Mar. 7, 2019
• Author registration deadline: Mar. 31, 2019
• Camera-ready deadline: Mar. 31, 2019

Information about IEEE CEC 2019: http://cec2019.org/#

Special Session Organizers:

Dr. Manoranjan Mohanty, University of Auckland, New Zealand
Prof. Ajit Narayanan, Auckland University of Technology, New Zealand
Dr. Mukesh Prasad, University of Technology Sydney, Australia

Contact (Regarding this Special Session):

Manoranjan Mohanty
Email: [first letter of first name] . [last name] @auckland.ac.nz

We look forward to receiving your high quality submissions!

CFP: IEEE CEC 2019 Special Session on Brain Storm Optimization Algorithms

The Brain Storm Optimization (BSO) algorithm is a new kind of swarm intelligence algorithm, which is based on the collective behavior of human being, that is, the brainstorming process. There are two major operations involved in BSO, i.e., convergent operation and divergent operation. A “good enough” optimum could be obtained through recursive solution divergence and convergence in the search space. The designed optimization algorithm will naturally have the capability of both convergence and divergence.

BSO possess two kinds of functionalities: capability learning and capacity developing. The divergent operation corresponds to the capability learning while the convergent operation corresponds to capacity developing. The capacity developing focuses on moving the algorithm's search to the area(s) where higher potential solutions may exist while the capability learning focuses on its actual search towards new solution(s) from the current solution for single point based optimization algorithms and from the current population of solutions for population-based swarm intelligence algorithms. The capability learning and capacity developing recycle to move individuals towards better and better solutions. The BSO algorithm, therefore, can also be called as a developmental brain storm optimization algorithm.

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

The BSO algorithm can also be seen as a combination of swarm intelligence and data mining techniques. Every individual in the brain storm optimization algorithm is not only a solution to the problem to be optimized, but also a data point to reveal the landscapes of the problem. The swarm intelligence and data mining techniques can be combined to produce benefits above and beyond what either method could achieve alone.

Topics of Interest

This special session aims at presenting the latest developments of BSO algorithm, 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 algorithms are very welcome for this Special session.
Potential topics include, but are not limited to:

• Theoretical aspects of BSO algorithms;
• Analysis and control of BSO parameters;
• Parallelized and distributed realizations of BSO algorithms;
• BSO for multiple/many objective optimization;
• BSO for constrained optimization;
• BSO for discrete optimization;
• BSO for large-scale optimization;
• BSO algorithm with data mining techniques;
• BSO in uncertain environments;
• BSO for real-world applications.

Submission

Please follow the IEEE CEC2019 instruction for authors and submit your paper via the IEEE CEC 2019 online submission system. Please specify that your paper is for the Special Session on Brain Storm Optimization Algorithms.

Organisers

Shi Cheng, Shaanxi Normal University, Xi’an, China, cheng#snnu.edu.cn
Junfeng Chen, Hohai University, Changzhou, China, chen-1997#163.com
Yuhui Shi, Southern University of Science and Technology, Shenzhen, China, shiyh#sustc.edu.cn

CFP: IEEE CEC 2019 Special Session on Multimodal Multiobjective Optimization

In multiobjective optimization problems, there may exist two or more distinct Pareto optimal sets (PSs) corresponding to the same Pareto Front (PF). These problems are defined as multimodal multiobjective optimization problems (MMOPs). Arguably, finding one of these multiple PSs may be sufficient to obtain an acceptable solution for some problems. However, failing to identify more than one of the PSs may prevent the decision maker from considering solution options that could bring about improved performance.

Given that the study of multimodal multiobjective optimization (MMO) is still in its emerging stages, although a few MMO algorithms have been proposed, there is still no public test function platform to compare these algorithms.

The organizers hope that the special session will motivate other researchers to promote the design of novel algorithms to solving MMOPs and the method for MMO test problems.

Scope

Topics of interest may cover, but are not limited to:

• Evolutionary algorithms for multimodal multiobjective optimization
• Hybrid algorithms for multimodal multiobjective optimization
• Adaptable algorithms for multimodal multiobjective optimization
• Surrogate techniques for multimodal multiobjective optimization
• Machine learning methods helping to solve multimodal multiobjective optimization problems
• Memetic computing for multimodal multiobjective optimization
• Niching techniques for multimodal multiobjective optimization
• Parallel computing for multimodal multiobjective optimization
• Design methods for multimodal multiobjective optimization test problems
• Decision making in multimodal multiobjective optimization
• Related theory analysis
• Applications

Submissions

Papers should be submitted following the instructions at the IEEE CEC 2019 web site. Please select the main research topic as the Special Session on “multimodal multiobjective optimization”. Accepted papers will be included and published in the conference proceedings.

• Deadline: 7th January, 2019
• Notification: 7th March 2019

Information on the format and templates for papers can be found here: http://www.cec2019.org/papers.html#templates

The attachment 'Parts of MMO test problems.zip' includes some of the MMO test problems. More MMO test problems and performance indicators will be given in the near future. Please pay close attention to our updating information.

Organizers

Dr. Jing Liang
Professor, Zhengzhou University, Zhengzhou, China
liangjing@zzu.edu.cn

Dr. Boyang Qu
Associate Professor, Zhongyuan University of Technology, Zhengzhou, China
Email: qby1984@hotmail.com

Dr. Dunwei Gong
Professor, China University of Mining and Technology, Xuzhou, China
dwgong@vip.163.com

Further information: http://www5.zzu.edu.cn/ecilab/info/1036/1163.htm

CFP: IEEE CEC 2019 Special Session on Evolutionary Computation for Music, Art, and Creativity

Creativity and Intelligence are both terms that have been deeply studied for centuries but still generate debates. Scholars frequently relate both terms, establishing connections that allows to understand the relationship between general intelligence and creativity. Both are considered required for addressing challenging problems, and also for creating art or appealing designs. Music, Literature, Architecture, Painting, Crafts, Industrial Design,... all could benefit from a better understanding and conceptualization of the processes behind Creativity and Intelligence. Although computers have exceeded the capabilities of humans in a number of limited domains, human creativity generally remains unchallenged, and only recently some techniques, such as Computational Intelligence, have begun to address problems related to creativity. Computational Intelligence (CI) is a term that embodies a number of nature-inspired techniques. CI includes Evolutionary Computation, Neural Networks, Fuzzy logic Systems and other techniques derived from them, such as Swarm Optimization, Artificial Immune Systems, Ant Colony Optimization to name but a few. CI is routinely applied nowadays to solving complex real life problems. Despite the great variety of methods and applications, only very recently, researchers have considered the capabilities of CI when applied to creative processes. Nevertheless, the finding of a general model for creativity and its relationship with Intelligence is far to be found.

Goals

This task force aims at promoting the study of Creativity and its connection to Intelligence from the point of view of Computational Intelligence. The task force will promote the study of computational creative discovery by means of CI, with the aim of both enhancing human creativity and also generation of autonomous creative behaviors. Artist creation will be an area of research: we will pay attention to visual art and music composition. We will pursue the application of CI to any branch of Art and Design, included but not limited to Architecture, Painting, Music, Literature, to name but a few.

The task force will also be interested in the study of the underlying mental processes leading to creativity, and their translation to hardware and software implementation.

The task force will be appealing for researchers from a variety of disciplines and backgrounds, with coverage across the arts and sciences, with interest in the application of an interdisciplinary approach.

Scope

The scope of this task force include the following topics:

• Contribute to fundamental understanding of artistic creativity.
• Contribute to Computational Intelligence approaches to creativity in humans and machines.
• Develop new CI based methodologies for generation of music, visual art, literature, architecture, and industrial design.
• Develop new methodologies based on evolutionary ecosystems dynamics for creative discovery.
• Develop new methodologies allowing the interaction between human and computer based creativity.
• Studying hardware platforms and software implementation leading to better creative systems.

Chair
Chuan-Kang Ting, National Chung Cheng University, Taiwan

Vice-Chair
Francisco Fernández de Vega, University of Extremadura, Spain

Further information: http://cilab.cs.ccu.edu.tw/ci-tf/

CFP: 2019 IEEE CEC Special Session on Evolutionary Computation in Finance and Economics

Supported by the IEEE CIS Computational Finance and Economics Technical Committee

Important Dates

Submission Deadline: 7 January 2019
Acceptance Notification: 7 March 2019 

Scope and Topics: 

Computational Finance and Economics covers a wide area of topics and techniques. The arrival of new computational methods, especially from Evolutionary Computation (EC), continually pushes the boundaries of the field outwards. That, together with the advances in available hardware, have contributed to a growing interest in applying EC techniques to solve different financial and economics problems. This Special Session is dedicated to the application of Evolutionary Computation methodologies to such problems. We welcome papers from any algorithm from the EC field, as well as hybrid EC methods. Applications include (but not limited to):

• Algorithmic trading
• Artificial stock markets
• Agent-based models
• Digital currencies
• Financial forecasting
• Financial engineering
• Financial networks
• Insurance
• Portfolio selection and management
• Pricing complex financial products
• Risk management systems 

Paper Submission:

Please follow the IEEE CEC2019 Submission Web Site. Special session papers are treated the same as regular conference papers. Please specify that your paper is submitted to the Special Session in Evolutionary Computation in Finance and Economics. All papers accepted and presented at CEC2019 will be included in the conference proceedings published by IEEE Explore, which are typically indexed by EI.

Organizers

Michael Kampouridis
University of Kent, Medway, UK
Email: M.Kampouridis [at] kent [dot] ac [dot] uk

Fernando Otero
University of Kent, Medway, UK
Email: F.E.B.Otero [at] kent [dot] ac [dot] uk

CFP: 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'2019)

New Orleans, Louisiana, USA, June 23-26, 2019
http://sites.ieee.org/fuzzieee-2019/


Activity Deadline

Special sessions, tutorials, competitions, and panel session proposals:  
    October 08, 2018

Notification of acceptance for tutorials, special sessions, and panels:      
    November 02, 2018

Full paper submission:
    January 11, 2019

Notification of paper acceptance:
    March 04, 2019

Camera-ready paper submission:
    April 01, 2019

Early registration:
    April 05, 2019



The 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE
2019), the world-leading event focusing on the theory and
application of fuzzy logic, will be held in New Orleans,
Louisiana, USA. Nicknamed the "Big Easy", New Orleans is known for
its round-the-clock nightlife, vibrant live-music scene, and spicy
Cajun cuisine. It is located on the Mississippi River, near the
Gulf of Mexico, and is popular tourist destination for all ages.

FUZZ-IEEE 2019 will be hosted at the JW Marriott, a premier
conference venue nestled in the heart of the world-famous French
Quarter. You will be steps away from some of New Orleansís most
iconic nightlife and restaurants. Take a walk outside and visit
Jackson Square, shop at the lively French Market, or dance your
Way through Bourbon Street.

FUZZ-IEEE 2019 will represent a unique meeting point for
scientists and engineers, from academia and industry, to interact
and discuss the latest enhancements and innovations in the field.
The topics of the conference will cover all aspects of theory and
applications of fuzzy logic and its hybridisations with other
artificial and computational intelligence methods.

FUZZ-IEEE 2019 will represent a unique meeting point for scientists
and engineers, both from academia and industry, to interact and
discuss the latest enhancements and innovations in the field. The
topics of the conference will cover all the aspects of theory and
applications of fuzzy logic and its hybridizations with other
artificial and computational intelligence techniques. In particular,
FUZZ-IEEE 2019 topics include, but are not limited to:

• Mathematical and theoretical foundations of fuzzy sets, fuzzy measures and fuzzy integrals
• Fuzzy control, robotics, sensors, fuzzy hardware and architectures
• Fuzzy data analysis, fuzzy clustering, classification and pattern recognition
• Type-2 fuzzy sets, computing with words and granular computing
• Fuzzy systems with big data and cloud computing, fuzzy analytics and visualization
• Fuzzy systems design and optimization
• Fuzzy decision analysis, multi-criteria decision making and decision support
• Fuzzy logic and its applications in Industrial Engineering
• Fuzzy modelling, identification and fault detection
• Fuzzy information processing, information extraction and fusion
• Fuzzy web engineering, information retrieval, text mining and social network analysis
• Knowledge discovery, learning, reasoning and knowledge representation
• Fuzzy image, speech and signal processing, vision and multimedia data
• Fuzzy databases and information retrieval
• Rough sets, imprecise probabilities, possibilities approaches
• Industrial, financial, and medical applications
• Fuzzy logic applications in civil engineering, geographical information systems
• Fuzzy sets and soft computing in social sciences
• Linguistic summarization, natural language processing
• Computational Intelligence in security systems
• Hardware/Software for fuzzy systems
• Fuzzy Markup Language and standard technologies for fuzzy systems
• Adaptive, hierarchical and hybrid (neuro- and evolutionary-) fuzzy systems

Conference Chairs:

• Tim Havens, Michigan Technological University, USA
• Jim Keller, University of Missouri, USA

CFP: IEEE CEC 2019 Special Session on Games

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. Games scale from simple problems for developing algorithms to incredibly hard problems for testing algorithms to the limit. 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 as well as tabletop games, board games, and puzzles. This special session aims at gathering leaders and neophytes in games research 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 methods (evolutionary computation, supervised learning, unsupervised learning, fuzzy systems, game-tree search, rolling horizon algorithms, MCTS, etc.) to games (card games, board games, mathematical games, action games, strategy games, role-playing games, arcade games, serious games, etc.).

Examples include but are not limited to

• 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 algorithms
• Imitating human players
• Learning to play games
• Multi-agent and multi-strategy learning
• Player/opponent modelling
• Procedural content generation
• CI for Serious Games (e.g., games for health care, education or training)
• Results of game-based CI and open competitions

Important Dates

Submission deadline:7 January 2019
Notification: 7 March 2019
Final paper submission: 31 March 2019

Submission Guidelines

Special session papers should be uploaded online through the paper submission website of IEEE CEC 2019. Please select the corresponding special session name ("Games") as the "main research topic" in submission. For the latest information on important dates, please refer to this page.

Organizers

Jialin Liu, liujl(at)sustc.edu.cn
Research Assistant Professor, Dept. of Computer Science and Engineering, Southern University of Science and Technology, China

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

CFP: IEEE CEC 2019 Special Session on Evolutionary Computation for Automated Algorithm Design

Computational intelligence systems play an imperative role in solving real world complex problems in industry. These systems have contributed to many facets of industry including data mining, transportation, health systems, computer vision, computer security, robotics, software engineering scheduling, and amongst others. Computational intelligence systems employ one or more computational intelligence techniques such as neural networks, fuzzy logic, genetic algorithms, multi-agent approaches and rule-based systems. Implementation of these techniques require a number of design decisions to be made, e.g. what architecture to use, what parameter values to use, and derivation of problem specific operators. It may also be necessary to employ a hybrid system combining techniques to solve a problem which introduces additional decisions such as which techniques to use and how to combine these techniques. This makes the development of computational systems time consuming, requiring extensive expertise, and many man hours. Consequently, there have been a number of initiatives to automate these processes.

There has been a fair amount of research into parameter tuning and control. The field of auto-machine learning aims to automate the design of machine learning algorithms so as to produce off-the-shelf machine learning techniques. Attempts to automate neural network architecture design has led to the field of neuroevolution. Research in this area has also been directed at inducing fuzzy functions, rule-based systems and multi-agent architectures. Hyper-heuristics, which were initially aimed at providing generalized solutions to combinatorial optimization problems, are proving to be effective in the automated development of techniques such as metaheuristics. Evolutionary algorithms such as genetic programming and genetic algorithms have chiefly been used in these initiatives. The aim of this special session is to examine recent developments in the field and future directions including the challenges and how these can be overcome.

The topics covered include, but are not limited to, the use of evolutionary algorithms for the following:

• Parameter control and tuning
• Architecture design, e.g. design of neural network and multi-agent architectures
• Automated hybridization of intelligent techniques
• Derivation of operators
• Derivation of construction heuristics
• Derivation of evaluation functions
• Automatic system development using hyper-heuristics
• Automatic programming
• Auto-ML
• Search-based software engineering
• Neuroevolution

Organizers: 

Nelishia Pillay,
University of Pretoria, South Africa
E-mail: npillay@up.ac.za

Rong Qu,
University of Nottingham, UK
E-mail: Rong.Qu@nottingham.ac.uk

Important Dates:  

Paper submission deadline: 7 January, 2019
Paper acceptance notification: 7 March, 2019
Final paper submission deadline: 31 March, 2019
Early registration: 31 March, 2019

Paper Submission: 

Special session papers are treated the same as regular papers and must be submitted via the CEC 2019 submission website. When submitting choose the "Evolutionary Computation for Automated Algorithm Design" special session from the "Main Research Topic" list.

CFP: IEEE CEC 2019 Special Session on Data-Driven Evolutionary Optimization of Computationally Expensive Problems

Meta-heuristic algorithms, including evolutionary algorithms and swarm optimization, face challenges when solving time-consuming problems, as typically these approaches require thousands of function evaluations to arrive at solutions that are of reasonable quality. Surrogate models, which are computationally cheap, have in recent years gained in popularity in assisting meta-heuristic optimization, by replacing the compute-expense/time-expensive problem during phases of the heuristic search. However, due to the curse of dimensionality, it is very difficult, if not impossible to train accurate surrogate models. Thus, appropriate model management techniques, memetic strategies and other schemes are often indispensable. In addition, modern data analytics involving advance sampling techniques and learning techniques such as semi-supervised learning, transfer learning and active learning are highly beneficial for speeding up evolutionary search while bringing new insights into the problems of interest. This special session aims at bringing together researchers from both academia and industry to explore future directions in this field. 

Scope and Topics

The topics of this special issue include but are not limited to the following topics:

• Surrogate-assisted evolutionary optimization for computationally expensive problems
• Adaptive sampling using machine learning and statistical techniques
• Surrogate model management in evolutionary optimization
• Data-driven optimization using big data and data analytics
• Knowledge acquisition from data and reuse for evolutionary optimization
• Computationally efficient evolutionary algorithms for large scale and/or many-objective optimization problems
• Real world applications including multi-disciplinary optimization.

Important Dates

Submission deadline:7 January 2019
Notification: 7 March 2019
Final paper submission: 31 March 2019
Early Registration: 31 March 2019

Submissions

The papers must be submitted online through the manuscript submission system (http://cec2019.org/papers.html#submission). 

Organizers

Prof. Chaoli Sun, Department of Computer Science and Technology, Taiywan University of Science and Technology, China. 

Prof. Jonathan Fieldsend, Department of Computer Science, University of Exeter, United Kingdom. 

Prof. Yew-Soon Ong, School of Computer Engenieering, Nanyang Technological University, Singapore. 

CFP: IEEE TEVC Special Issue on Parallel Evolution for Large Scale Optimization (Nov 1)

I. AIM AND SCOPE

Human societies have entered a new era of intelligent tech- nology, where machines, information, and humans are tightly coupled in the large scale cyber-physical-social spaces (CPSS). As a result, a lot of large-scale problems, such as optimization and learning, are emerging with the aim to explore and exploit of the physical world, mental world and virtual world. With the dramatic advances in big data analytics, communications, computing and data storage, it is expected that Evolutionary Computation (EC), as a powerful approach to complex prob- lems, would play an even more important role in CPSS. This could be achieved through advances in several aspects, such as developing more powerful EC techniques for large-scale optimization problems, bridging EC and emergent techniques in CPSS (e.g., the theory and methods of parallel systems) to offer new mechanisms for managing and controlling complex systems that involve complexity issues of both engineering and social dimensions, and building large-scale evolution systems that are capable of describing, predicting and prescribing the evolution of real-world complex systems. This special issue aims at promoting the development of EC in the above aspects.

II. THEMES

Researchers are encouraged to submit their latest inves- tigations on EC, either fundamental advances or practical cases, for large-scale problems as well as systems to the special issue. In addition to advancements of EC for large- scale optimization, learning and other challenging problems that arise in complex systems, research on building large-scale evolutionary systems for simulation, management and control of cyber-physical-social systems are most welcome as well.

Topics of interest include (but are not limited to):

• Evolutionary Computation for Large-Scale Optimization Problems;
• Evolutionary Computation for Large-Scale Learning Problems;
• Evolutionary Computation for Complex Systems;
• Evolutionary Computation for Optimal Management and
• Control in CPSS;
• Theoretical Analysis on Evolutionary Computation for
• Large-Scale Problems and Systems;
• Adaptation and Learning Mechanisms for large-scale
• evolutionary systems;
• Parallel Evolutionary Computation Techniques;
• New Implementation Technologies of Evolutionary Computation for Emerging Large-Scale problems;
• New Trends for Evolutionary Computation in Large Scale Optimization.

III. SUBMISSION

  Manuscripts should be prepared according to the “In- formation for Authors” section of the journal found at http://cis.ieee.org/ieee-transactions-on-evolutionary-computation/.

  Please submit your manuscript in electronic form through: http://mc.manuscriptcentral.com/tevc-ieee/, by selecting “PEforLSO Special Issue Papers” as theManuscript Type. Also, please indicate “PEforLSO Special Issue Paper” in the comments to the Editor-in-Chief.

  Submitted papers will be reviewed by at least three different experts. Submission of a manuscript implies that it is the authors’ original unpublished work and is not being submitted for possible publication elsewhere.

IV. IMPORTANT DATES

Submission open: May 15, 2018

Submission deadline: November 1, 2018

Tentative publication date: 2019

For further information, please contact one of the following Guest Editors.

V. GUEST EDITORS

• Fei-Yue Wang, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, China, and Qingdao Academy of Intelligent Industries, China

feiyue.wang@ia.ac.cn

• Qinglai Wei, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, China

qinglai.wei@ia.ac.cn

• Ke Tang, Department of Computer Science and Engineering, Southern University of Science and Technology, China tangk3@sustc.edu.cn

• Carlos A. Coello Coello, Department of Computer Science, CINVESTAV-IPN, Mexico

ccoello@cs.cinvestav.mx

CFP: IEEE TETCI Special Issue on Big Data and Computational Intelligence for Agile Wireless IoT (Oct 15)

I. AIM AND SCOPE

  Wireless networking technology is one of the main components that could empower a wide range of Internet-of- Things (IoT) applications including smart city, smart home, smart grid, e-health, smart transportation, etc. While providing an easily extensible solution for information exchange, wireless networks also have brought some crucial challenges due to the unstable characteristics of wireless communications.

  The first challenge, namely the spatial challenge, comes from the massive number of spatially-spread connected static or mobile devices affected by the limitations and disruptions of the operating environment, including propagation media, disasters, infrastructure failures, and so on. The second challenge, namely the temporal challenge, is due to the time evolution of the temporal features, such as the varying traffic rates, different quality-of-service requirements, and the state changes of the operating environment. Both spatial and temporal challenges can possibly be solved by using Computational intelligence (CI) technologies such as fuzzy logic, artificial neural networks, evolutionary computation, learning theory, probabilistic methods, and so on. On the other hand, big data-based approaches, including deep neural networks and Long Short- Term Memory networks, could facilitate data-driven prediction and performance improvement by capturing time-dependent properties of network elements such as user traffics and behaviors. Meanwhile, new CI technologies should be discussed in order to handle the large volume of IoT big data from various types of devices with different generation speeds and characteristics.

  The design and the operation of a wireless network can benefit from data collected from widely deployed sensors, network devices, social networks, and other sources to address the spatial and temporal challenges. We refer collectively to these data sources as “IoT big data” for convenience. These data can be highly dimensional, heterogeneous, complex, unstructured and unpredictable. The ready availability of IoT big data and the immense dividends on offer motivate a strong interest both in academia and in industry towards solving some of the vexing challenges that stand in the way of leveraging IoT big data to advance the state of the art in wireless network operations and applications.

  CI technologies are expected to provide efficient and powerful tools that scale well with data volume for IoT big data analytics and process, while addressing the challenges brought by the massive amount of data. While CI technologies can achieve a flexible and self-evolving system design, big data can facilitate the use of deep neural networks which is possible to learn the best strategy from complex data. It is envisioned that the combination of IoT big data with a large collection of CI algorithms will reach the level of true agility in wireless IoT.

II. TOPICS

  This special issue focuses on solutions that can synergistically leverage techniques and insights from the domains of big data and CI to resolve the spatial and temporal challenges in wireless IoT, thereby significantly advancing the state of the art in design, operation, and analysis of data-driven wireless IoT. Topics of interest include, but are not limited to:

• CI-based solutions for spatial & temporal challenges in wireless IoT, including propagation challenges, MAC & routing problems, mobile edge computing issues, disasters, and infrastructure failures.
• Data-driven prediction and performance improvement for wireless IoT including deep neural networks, Long Short-Term Memory networks, etc.
• Joint neural networks and learning approaches, such as deep reinforcement learning, for addressing challenges in wireless IoT.
• CI technologies for handling a large volume of wireless IoT big data.
• Learning new flexible and self-evolving strategies for resource allocation, network management and planning by analyzing wireless IoT big data with CI.

III. IMPORTANT DATES

• Manuscript submission: October15,2018.
• Notification of authors:  January 15, 2019.
• Revised manuscripts due: March 15, 2019.
• Final editorial decision: May15,2019.

IV. SUBMISSION GUIDELINES

  Manuscripts should be prepared according to the “Information for Authors” section of the journal and submissions should be done through the journal manuscript submission system https://mc.manuscriptcentral.com/tetci-ieee, by selecting the Manuscript Type of “Big Data and Computational Intelligence for Agile Wireless IoT” and clearly marking “Special Issue on Big Data and Computational Intelligence for Agile Wireless IoT” as comments to the Editor-in-Chief.

V. GUEST EDITORS

• Celimuge Wu,  The University of Electro-Communications, Japan, celimuge@uec.ac.jp
• Yaochu Jin, University of Surrey, United Kingdom, yaochu.jin@surrey.ac.uk
• Jie Li, University of Tsukuba, Japan, lijie@cs.tsukuba.ac.jp
• Kok-Lim Alvin Yau, Sunway University, Malaysia, koklimy@sunway.edu.my
• Junaid Qadir, Information Technology University, Pakistan, junaid.qadir@itu.edu.pk

CFP: 2019 IEEE CEC Session on Evolutionary Computation for Granular Computing

2019 IEEE Congress on Evolutionary Computation (CEC2019)

Wellington, New Zealand, 10-13 June 2019

Introduction

With the over-flooding of big data, researchers and practitioners have started showing remarkable interest to explore the data space, and have considered that structuralized knowledge reasoning is an effective computational paradigm for dealing with big data tasks. Granular computing (GC) focuses on the knowledge representation and reasoning with information granules, and fuzzy sets and rough sets are two crucial branches of GC. Fuzzy sets theory was introduced to represent concepts with ambiguous boundaries and to understand the processes of complex human reasoning. It has become a popular tool for the design of fuzzy classifiers. Rough sets theory was presented to quantitatively analyze the uncertainty and to process incomplete knowledge. It can find a decision-making table between the strict statistics and random distribution. Since rough set theory can typically describe the uncertainty of knowledge, it has been extensively used in data mining, knowledge discovery, and intelligent system. It is a promising line of work for the design of efficient granular computing model and method for handling big data.

The global search performed by evolutionary computation algorithms frequently provides a valuable complement to the local search of non-evolutionary methods, and combinations of granular computing and evolutionary computation often show particular promise in practice. Evolutionary computation for granular computing emphasizes the utility of different evolutionary algorithms to various facets of granular computing, ranging from theoretical analysis to real-life applications. The main motivation for applying evolutionary algorithms to granular computing tasks in the knowledge reasoning is that they are robust and adaptive search methods, which can perform a global search in the space of candidate solutions. It has been a hot trend to address the classical and new-emerging granular computing problems by using different evolutionary algorithms. The benefits of exploring the combination of granular computing and evolutionary computation in the knowledge reasoning scenario will have an impact in multiple research disciplines and industry domains, including transportation, communications, social network, medical health, and so on.

Aim and Scope

The goal of this special section aims at providing a specific opportunity to review the state-of-the-art of evolutionary computation for granular computing, and bringing together researchers in the relevant areas to discuss the latest progress, new research methodologies and potential research topics. The selected papers will be beneficial to both academia and industry, for delivering the latest research results and inspiring new directions to study.

The topics of interest include, but are not limited to:

• Fuzzy sets method and system with evolutionary algorithm
• Rough sets method and system with evolutionary algorithm
• Probabilistic granules model with evolutionary algorithm
• Shadowed sets model with evolutionary algorithm
• Multi-objective evolutionary algorithm for granular computing
• Evolutionary fuzzy deep neural network for data classification
• Granular computing framework for big data analytic by evolutionary algorithm
• Evolutionary multimodal optimization for fuzzy rough system
• Evolutionary multimodal optimization for rough fuzzy system
• Quantum-inspired evolutionary algorithm for granular computing
• Co-evolutionary algorithm for granular computing framework
• Adaptive granular computing framework with evolutionary algorithm
• Convergence analysis of evolutionary algorithm for granular computing
• Evolutionary optimization with dynamic parameter adaptation for fuzzy system
• Granular data mining for feature learning, classification, regression, and clustering with evolutionary algorithm
• Granular data mining for multi-task modeling, multi-view modeling and co-learning with evolutionary algorithm
• Real-world applications using evolutionary granular computing methods

Submissions

Papers should be submitted following the instructions at the IEEE CEC 2019 web site. Please select the main research topic as the Special Session on “Evolutionary Computation for Granular Computing”. All papers accepted and presented at CEC2019 will be included in the conference proceedings published by IEEE Explore, which are typically indexed by EI. Information on the format and templates for papers can be found here:http://www.cec2019.org/papers.html#templates.

Important dates

• Paper submission: 7 January, 2019
• Decision notification: 7 March, 2019
• Camera ready paper due: 31 March, 2019
• Registration: 31 March, 2019
• Conference: 10 June, 2019

Session Organizers

Weiping Ding

Nantong University, China.

Email address: dwp9988@hotmail.com

Gary G. Yen

Oklahoma State University, U.S.A.

Email address: gyen@okstate.edu

Biography of the Organizers

Session Organizer 1

Weiping Ding (M’16) received the Ph.D. degree in Computation Application, Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2013. He is an Associate Professor, Deputy Head of Computer Science and Technology Department, Nantong University, China. His research interests include fuzzy sets, rough sets, evolutionary computation, data mining, machine learning, and big data analytics. He was a Visiting Researcher at University of Lethbridge, Alberta, Canada, in 2011. From 2014 to 2015, He is a Postdoctoral Researcher at the Brain Research Center, National Chiao Tung University (NCTU), Hsinchu, Taiwan, China. In 2016, He was a Visiting Scholar at National University of Singapore (NUS), Singapore. From 2017 to 2018, he was a Visiting Scholar at University of Technology Sydney (UTS), Australia. He is a member of IEEE, ACM and Senior CCF.

Dr. Ding has published over 60 papers in top journals and prestigious conferences as the first author, including IEEE Transactions on Fuzzy Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Information Sciences, Expert System with Application, Knowledge-Based Systems, Neurocomputing and so on. To data, he has held 10 approved invention patents in total over 20 issued patents. Dr. Ding was a recipient of Computer Education Excellent Paper Award (First-Prize) from the National Computer Education Committee of China, in 2009. He was an Excellent-Young Teacher (Qing Lan Project) of Jiangsu Province in 2014, and a High-Level Talent (Six Talent Peak) of Jiangsu Province in 2016. He was awarded the Best Paper of ICDMA’15, and an Outstanding Teacher of Software Design and Entrepreneurship Competition by the Ministry of Industry and Information Technology, China, in 2017. Dr. Ding was a recipient of the Medical Science and Technology Award (Second-Prize) in 2017, and Jiangsu Provincial Education Teaching and Research Achievement Award (Third-Prize). Dr. Ding was awarded two Chinese Government Scholarships for Overseas Studies in 2011 and 2016.

Dr. Ding served /serves as an Associate Editor of IEEE Transactions on Fuzzy Systems, Information Sciences, Swarm and Evolutionary Computation, and IEEE Access. He severs as a Reviewer in Top-tier Journals such as IEEE Transactions on Fuzzy Systems, IEEE Transactions on Neural Networks and Learning System, IEEE Transactions on Cybernetics, IEEE Transactions on Knowledge and Data Engineering, Information Sciences and so on. He has been serving as a steering committee member and a program committee member for a number of international conferences.

Session Organizer 2

Gary G. Yen received his PhD degree in electrical and computer engineering from the University of Notre Dame in 1992. He worked at the Structural Control Division of the USAF Research Laboratory in Albuqurque during 1992-1996. He is currently a Professor in the School of Electrical and Computer Engineering, Oklahoma State University in Stillwater. His research is supported by the DoD, DoE, EPA, NASA, NSF, and Process Industry. His research interest includes intelligent control, computational intelligence, conditional health monitoring, signal processing and their industrial/defense applications.

Dr. Yen was an associate editor of the IEEE Control Systems Magazine, IEEE Transactions on Control Systems Technology, Automatica, Mechantronics, IEEE Transactions on Systems, Man and Cybernetics, Part A and Part B, IEEE Transactions on Neural Networks, and among others. He is currently serving as an associate editor for the IEEE Transactions on Evolutionary Computation and International Journal of Swarm Intelligence Research. He served as the General Chair for the 2003 IEEE International Symposium on Intelligent Control held in Houston and 2006 IEEE World Congress on Computational Intelligence held in Vancouver. Dr. Yen served as Vice President for the Technical Activities in 2005-2006 and President in 2010-2011 of the IEEE Computational Intelligence Society. He is the founding Editor-in-Chief of the IEEE Computational Intelligence Magazine.

He received KC Wong Fellowship from the Chinese Acadamy of Sciences, Halliburton Outstanding Faculty award, and OSU Regents Distinguished Research award. He also received an Honorary Professorship from Northeastern University, Sichuan University, and Dalian University of Technology in China. In 2011, he received the Andrew P Sage Best Transactions Paper award from the IEEE Systems, Man and Cybernetics Society. In 2013, he received Meritorious Service award from the IEEE Computational Intelligence Society. He is a distinguished lecturer from the IEEE Computational Intelligence Society, 2012-2014, an IEEE Fellow, and IET Fellow.

CFP: IEEE CIM Special Issue on Deep Reinforcement Learning and Games (Oct 1)

AIMS AND SCOPE

Recently, there has been tremendous progress in artificial intelligence (AI) and computational intelligence (CI) and games. In 2015, Google DeepMind published a paper “Human-level control through deep reinforcement learning” in Nature, showing the power of AI&CI in learning to play Atari video games directly from the screen capture. Furthermore, in Nature 2016, it published a cover paper “Mastering the game of Go with deep neural networks and tree search” and proposed the computer Go program, AlphaGo. In March 2016, AlphaGo beat the world’s top Go player Lee Sedol by 4:1. In early 2017, the Master, a variant of AlphaGo, won 60 matches against top Go players. In late 2017, AlphaGo Zero learned only from self-play and was able to beat the original AlphaGo without any losses (Nature 2017). This becomes a new milestone in the AI&CI history, the core of which is the algorithm of deep reinforcement learning (DRL). Moreover, the achievements on DRL and games are manifest. In 2017, the AIs beat the expert in Texas Hold’em poker (Science 2017). OpenAI developed an AI to outperform the champion in the 1V1 Dota 2 game. Facebook released a huge database of StarCraft I. Blizzard and DeepMind turned StarCraft II into an AI research lab with a more open interface. In these games, DRL also plays an important role.
The theoretical analysis of DRL, e. g., the convergence, stability, and optimality, is still in early days. Learning efficiency needs to be improved by proposing new algorithms or combining with other methods. DRL algorithms still need to be demonstrated in more diverse practical settings. Specific topics of interest include but are not limited to:

• Survey on DRL and games;
• New AI&CI algorithms in games;
• Learning forward models from experience;
• New algorithms of DL, RL and DRL;
• Theoretical foundation of DL, RL and DRL;
• DRL combined with search algorithms or other learning methods;
• Challenges of AI&CI games as limitations in strategy learning, etc.;
• DRL or AI&CI Games based applications in realistic and complicated systems.

IMPORTANT DATES

Submission Deadline: October 1st, 2018
Notification of Review Results: December 10th, 2018

Submission of Revised Manuscripts: January 31st, 2019

Submission of Final Manuscript: March 15th, 2019

Special Issue Publication: August 2019 Issue

GUEST EDITORS

D. Zhao, Institute of Automation, Chinese Academy of Sciences, China, Dongbin.zhao@ia.ac.cn

S. Lucas, Queen Mary University of London, UK, simon.lucas@qmul.ac.uk

J. Togelius, New York University, USA, julian.togelius@nyu.edu.

SUBMISSION INSTRUCTIONS

1. The IEEE CIM requires all prospective authors to submit their manuscripts in electronic format, as a PDF file. The maximum length for Papers is typically 20 double-spaced typed pages with 12-point font, including figures and references. Submitted manuscript must be typewritten in English in single column format. Authors of Papers should specify on the first page of their submitted manuscript up to 5 keywords. Additional information about submission guidelines and information for authors is provided at the IEEE CIM website. Submission will be made via https://easychair.org/conferences/?conf=ieeecimcitbb2018.
2. Send also an email to guest editor D. Zhao (dongbin.zhao@ia.ac.cn) with subject “IEEE CIM special issue submission” to notify about your submission.
3. Early submissions are welcome. We will start the review process as soon as we receive your contribution.

CFP: IEEE TEVC Special Issue on Theoretical Foundations of Evolutionary Computation (Oct 1)

I. AIM AND SCOPE

  Evolutionary computation (EC) methods such as evolutionary algorithms, ant colony optimization and artificial immune systems have been successfully applied to a wide range of problems. These include classical combinatorial optimization problems and a variety of continuous, discrete and mixed integer real-world optimization problems that are often hard to optimize by traditional methods (e.g., because they are non-linear, highly constrained, multi-objective, etc.). In contrast to the successful applications, there is still a need to understand the behaviour of these algorithms. The achievement and development of a solid theory of bio-inspired computation techniques is important as it provides sound knowledge on their working principles. In particular, it 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 ones are not. The benefits of theoretical understanding for practitioners are threefold. 1) Aiding algorithm design, 2) guiding the choice of the best algorithm for the problem at hand and 3) determining optimal parameter settings.

  The aim of this special issue is to advance the theoretical understanding of evolutionary computation methods. We solicit novel, high quality scientific contributions on theoretical or foundational aspects of evolutionary computation. A successful exchange between theory and practice in evolutionary computation is very desirable and papers bridging theory and practice are of particular interest. In addition to strict mathematical investigations, experimental studies strengthening the theoretical foundations of evolutionary computation methods are very welcome.

II. THEMES

  This special issue will present novel results from different sub- areas of the theory of bio-inspired algorithms. The scope of this special issue includes (but is not limited to) the following topics:

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

  All classes of bio-inspired optimization algorithms will be considered including (but not limited to) evolutionary algorithms, ant colony optimization, artificial immune systems, particle swarm optimization, differential evolution, and estimation of distribution algorithms. All problem domains will be considered including discrete and continuous optimization, single-objective and multi-objective optimization, constraint handling, dynamic and stochastic optimization, co-evolution and evolutionary learning.

III. SUBMISSION

Manuscripts should be prepared according to the “Information for Authors” section of the journal found at http://ieee-cis.org/pubs/tec/authors/ and submissions should be made through the journal submission website: http://mc.manuscriptcentral.com/tevc-ieee/, by selecting the Manuscript Type of “TFoEC Special Issue Papers” and clearly adding “TFoEC Special Issue Paper” to the comments to the Editor-in-Chief.

Submitted papers will be reviewed by at least three different expert reviewers. Submission of a manuscript implies that it is the authors’ original unpublished work and is not being submitted for possible publication elsewhere.

Each submission will contain at least one paragraph explaining why the paper is (potentially) relevant to practice.

IV. IMPORTANT DATES

Submission open: February 1, 2018
Submission deadline: October 1, 2018
Tentative publication date: 2019

Papers will be assigned to reviewers as soon as they are submitted. Papers will be published online as soon as they are accepted.


For further information, please contact one of the following GuestEditors.

V. GUEST EDITORS

Pietro S. Oliveto


Department of Computer Science

University of Sheffield
United Kingdom

p.oliveto@sheffield.ac.uk


Anne Auger


INRIA

Ecole Polytechnique Paris

France

anne.auger@jnria.fr


Francisco Chicano


Department of Languages and Computing Sciences

University of Malaga


Spain
chicano@lcc.uma.es


Carlos M. Fonseca


Department of Informatics Engineering

University of Coimbra


Portugal
cmfonsec@dei.uc.pt

CFP: IEEE TETCI Special Issue on Computational Intelligence for Cellular/Wireless Communications and Sensing (Oct 1)

I. AIM AND SCOPE

  As billions of phones, appliances, drones, traffic lights, security systems, environmental sensors, radars, and other radio-connected sensing and communication devices sum into a rapidly growing Internet of Things (IoT), many challenges such as spectrum allocation and efficiency, energy efficiency, security, have emerged as urgent topics to be solved. For example, 5G wireless communications will be deployed in the 28GHz, 37GHz, 39GHz frequency band, which may co-exist with radars and other sensing devices. Quite often, researchers often handle these challenges using traditional approaches such as game theory, convex optimization, etc. Computational intelligences techniques such as fuzzy systems, evolutionary computing, neural networks and learning systems are capable of handling resources allocation, decision making, where uncertainties abound, so it is very natural to apply computational intelligence to the above challenges in cellular/wireless communications and sensing.  There are four important differences that make the emerging topics in Computational Intelligence for Cellular/Wireless Communications and Sensing (CICCS) unique.

1. 1)  Compared to traditional communication and sensing problems, the RF data rate is much higher in the emerging area of communication and sensing which means real-time decision such as resource allocation or signal detection should be made much faster based on computational intelligence.
2. 2)  The operating frequencies are much higher and users are heterogeneous.
3. 3)  RF waveforms are typically captured and represented as complex numbers, underscoring the importance of both amplitude and phase of the signal. Although there has been interest recently in complex-valued neural networks, the technology for learning naturally in the complex plane is not fully developed and relies on treating complex variables as two real numbers.
4. 4)  The integration of communication and sensing is highly desirable because the communication and sensing modules are often co-located such as in smart phones, and they may be operated in the same frequency band.

II. TOPICS

Topics of interest for this special issue include, but are not limited to:

• New computational intelligence models for communications and sensing
• Computational intelligence for 5G Communications Wireless
• Computational intelligence for IoT
• Computational intelligence for sensor networks
• Computational intelligence for remote sensing
• Computational intelligence for spectrum efficiency
• Computational intelligence for energy efficiency
• Computational intelligence for radars
• Computation intelligence for radar and communications co-existence
• Computational intelligence for integration of communications and sensing

III. SUBMISSIONS

Manuscripts should be prepared according to the “Information for Authors” section of the journal (http://cis.ieee.org/ieee-transactions-on-emerging-topics-in-computational-intelligence.html) and submissions should be done through the journal submission website: https://mc.manuscriptcentral.com/tetci-ieee, by selecting the Manuscript Type of “Computational Intelligence for Cellular/Wireless Communications and Sensing (SI:CICCS)” and clearly marking “Computational Intelligence for Cellular/Wireless Communications and Sensing (SI: CICCS) Special Issue Paper” as comments to the Editor-in-Chief. Submitted papers will be reviewed by at least three different expert reviewers. Submission of a manuscript implies that it is the authors’ original unpublished work and is not being submitted for possible publication elsewhere.

IV. IMPORTANT DATES

Paper submission deadline: October 1, 2018

Final notice of acceptance/reject: February 1, 2019

V. GUEST EDITORS

Qilian Liang, University of Texas at Arlington, USA;

liang@uta.edu

Gary Yen, Oklahoma State University, USA;

gyen@okstate.edu

Tariq S. Durrani, University of Strathclyde, UK;

durrani@strath.ac.uk

Wei Wang, Tianjin Normal University, China;

weiwang@tjnu.edu.cn

Xin Wang, Qualcomm Inc, USA;

xinwng@qca.qualcomm.com