CFP: IEEE CEC Special Session on Special Session on Computational Intelligence for Cybersecurity (CIC) (Jan 7th)

http://homepages.ecs.vuw.ac.nz/~alsahahari/cec19_ss_cic/

Although the rapid growth in technology and the Internet has simplified many different tasks in our daily life, this reliance on the Internet also makes us vulnerable to new types of security threats. Cybersecurity aims at preventing and detecting cyber attacks on Internet-connected systems which include data, software, and hardware, in order to maintain the confidentiality, integrity, and availability of those assets. On one hand, the diversity of attacks on such assets, which vary in nature, behavior and methodology makes the task of detecting such attacks more difficult. On the other hand, the limitation of having enough labelled data makes the task even harder to build a good model for researchers wanting to apply computational intelligence techniques. The lack of data makes transfer learning a promising paradigm where data from related (source) domains can be utilized to tackle the problem in the target domain to effectively increase the size of the labelled data sets.

Utilizing various evolutionary computation (EC) and machine learning (ML) techniques to tackle numerous problems related to cybersecurity have received increasing attention due to the success of such techniques to tackle problems in many other domains.

Scope and Topics

This interdisciplinary special session aims at providing a focused discussion forum for utilizing EC based techniques to automatically tackle different cybersecurity-related problems such as intrusion prevention and detection, malware detection, spam and phishing filtering, and other types of network-based attacks, e.g., DDoS (distributed denial of service). It also aims at promoting both practical applications and theoretical development of EC, e.g., genetic programming, evolutionary programing, genetic algorithms, particle swarm optimization, artificial immune systems, learning classifier systems, techniques for information and network security domains.

The scope of this special session covers, but not limited to, the following topics:

• Evolutionary Computation techniques
  • Data mining in cybersecurity
  • Evolutionary Transfer learning in cybersecurity
  • EC techniques for Feature extraction, selection and construction in cybersecurity
  • White-box and Black-box attacks
  • Adversarial machine learning
  • Online learning
  • Measurement and ground truth acquisition
  • Creation of synthesized training and test sets
  • Learning in games
• Security applications
• Automated vulnerability and penetration testing
• Ransomware, Spam and phishing detection
• Behavioral-based anomaly detection
• DDoS prediction and detection
• Authorship identification
• EC methods for Intrusion prevention and response
• Keystroke and other biometric dynamics
• Botnet detection
• Data anonymization/de-anonymization
• Vulnerability testing through intelligent probing (e.g. fuzzing)
• Privacy preserving data release
• Privacy preserving data publishing
• Location privacy
• Privacy analytics

Important Dates

• Paper Submission Deadline: 7 Jan 2019
• Notification of Acceptance: 7 Mar 2019
• Final Paper Submission Deadline: 31 Mar 2019

Paper Submission

Papers for IEEE CEC 2019 should be submitted electronically through the Congress website at http://www.cec2019.org/papers.html#submission, and will be refereed by experts in the fields and ranked based on the criteria of originality, significance, quality and clarity. To submit your papers to the special session, please select the Special Session name in the Main Research topic.

For more submission information please visit: http://cec2019.org/. All accepted papers will be published in the IEEE CEC 2019 electronic proceedings, included in the IEEE Xplore digital library, and indexed by EI Compendex.

Organisers

• Harith Al-Sahaf
  • School of Engineering and Computer Science, Victoria University of Wellington, New Zealand
  • harith.al-sahaf@ecs.vuw.ac.nz
• Ian Welch
  
  • School of Engineering and Computer Science, Victoria University of Wellington, New Zealand
  • ian.welch@ecs.vuw.ac.nz
• Zhen Ni
  • Electrical Engineering and Computer Science, South Dakota State University, USA
  • Zhen.Ni@sdstate.edu
• Wanlei Zhou
  • School of Software, University of Technology Sydney, Austarlia
  • Wanlei.Zhou@uts.edu.au

Harith Al-Sahaf received the B.Sc. degree in computer science from Baghdad University (Iraq), in 2005. He joined the Victoria University of Wellington (VUW), (New Zealand) in July 2007 where he received his MCompSc and PhD degrees in Computer Science in 2010 and 2017, respectively. In October 2016, he has joined the School of Engineering and Computer Science, VUW as a Post-doctoral Research Fellow and as a full-time lecturer since September 2018. His current research interests include evolutionary computation, particularly genetic programming, computer vision, pattern recognition, evolutionary cybersecurity, machine learning, feature manipulation including feature detection, selection, extraction and construction, transfer learning, domain adaptation, one-shot learning, and image understanding. He is a member of the IEEE CIS ETTC Task Force on Evolutionary Computer Vision and Image Processing, the IEEE CIS ETTC Task Force on Evolutionary Computation for Feature Selection and Construction, the IEEE CIS ISATC Task Force on Evolutionary Deep Learning and Applications, and the IEEE CIS ISATC Intelligent Systems for Cybersecurity.

Ian Welch has a PhD from the University of Newcastle upon Tyne. His current research includes machine learning for network security, IoT-specific security policies and honeypots. Prior to becoming an academic, he worked for a range of employers including the State Services Commission, Deloitte Touche Tohmatsu Limited, Accenture and the UK National Health System. He is a board member of the Faucet Foundation.

Zhen Ni is currently an Assistant Professor in Department of Electrical Engineering and Computer Science (EECS), South Dakota State University (SDSU), Brookings, SD. He received his Ph.D. degree from the Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island (URI), Kingston, RI, in 2015. He received B.S in Department of Control Science and Engineering (currently renamed as College of Automation), Huazhong University of Science and Technology (HUST), Wuhan, China, in 2010.

His research mainly includes Smart Grid, Computational Intelligence, Machine Learning, Adaptive Control, and Cyber-Physical Systems. He is very active in professional societies, including IEEE Computational Intelligence Society (CIS). For instance, he served as the General Chair for IEEE CIS winter school on Computational Intelligence for Big Data, Washington D.C. (2016), and Technical Program Co-Chair for IEEE International Conference on Cyber, Physical, and Social Computing (CPSCom), Halifax, Canada (2018). He also organized a special issue of Cyber-Physical Power Systems on IET Cyber Physical Systems: Theory & Applications (2017-2018). He is an Associate Editor for IEEE Computational Intelligence Magazine (IF: 6.343) from 2018.

He received the Chinese Government Award for Outstanding Students Abroad by Chinese government (2014), Second Prize of Graduate Student Poster Contest in IEEE Power and Energy Society General Meeting (2015), Enhancement of Graduate Research Award (EGRA) by URI (2014), Travel Award by IEEE SSCI-Doctoral Consortium (2014), National Encouragement Scholarship by Ministry of Education in China (2007), and all Outstanding Academic Students awards in HUST (2006-2010).

Wanlei Zhou received the B.Eng and M.Eng degrees from Harbin Institute of Technology, Harbin, China in 1982 and 1984, respectively, and the PhD degree from The Australian National University, Canberra, Australia, in 1991. He also received a DSc degree (a higher Doctorate degree) from Deakin University in 2002. He is currently the Head of School of Software in University of Technology Sydney (UTS). Before joining UTS, Professor Zhou held the positions of Alfred Deakin Professor, Chair of Information Technology, and Associate Dean of Faculty of Science, Engineering and Built Environment, Deakin University. Professor Zhou has been the Head of School of Information Technology twice (Jan 2002-Apr 2006 and Jan 2009-Jan 2015) and Associate Dean of Faculty of Science and Technology in Deakin University (May 2006-Dec 2008). Professor Zhou also served as a lecturer in University of Electronic Science and Technology of China, a system programmer in HP at Massachusetts, USA; a lecturer in Monash University, Melbourne, Australia; and a lecturer in National University of Singapore, Singapore. His research interests include security and privacy, bioinformatics, and e-learning. Professor Zhou has published more than 400 papers in refereed international journals and refereed international conferences proceedings, including many articles in IEEE transactions and journals.

Program Committee (TBC)

• Ryan Ko (The University of Waikato, New Zealand)
• Fabio Roli (University of Cagliari, Italy)
• Giovanni Russolo (The University of Auckland, New Zealand)
• Yufei Tang (Florida Atlantic University, USA)
• Vijay Varadharajan (The University of Newcastle, Australia)
• Bing Xue (Victoria University of Wellington, New Zealand)
• Jun Yan (University of Concordia, Canada)
• Roland Yap (National University of Singapore, Singapore)
• Tianqing Zhu (University of technology, Australia)
• Jun Zhang (Swinburne University of Technology, Australia)
• Mengjie Zhang (Victoria University of Wellington, New Zealand)

CFP: IEEE CEC 2019 Special Session on Evolutionary Computation in Healthcare Industry

Worldwide, the healthcare industry would continue to thrive and grow, because diagnosis, treatment, disease prevention, medicine, and service affect the mortal rates and life quality of human beings. Two key issues of the modern healthcare industry are improving healthcare quality as well as reducing economic and human costs. The problems in the healthcare industry can be formulated as
scheduling, planning, predicting, and optimization problems, where evolutionary computation methods can play an important role. Although evolutionary computation has been applied to scheduling and planning for trauma system and pharmaceutical manufacturing, other problems in the healthcare industry like decision making in computer-aided diagnosis and predicting for disease prevention have not properly formulated for evolutionary computation techniques, and many evolutionary computation techniques are not well-known to the healthcare community.

Scope and Topics

This special session aims to promote the research on evolutionary computation methods for their application to the healthcare industry. The topics of this special session include but are not limited to the following topics:

• Evolutionary computation in resource allocation for hospital location planning, aeromedical retrieval system planning, etc.
• Application of evolutionary computation for job scheduling, such as ambulance scheduling, nurse scheduling, job scheduling in medical device and pharmaceutical manufacturing, etc.
• Multiple-criteria decision-making for computer-aided diagnosis using expert systems.
• Web self-diagnostic system with the application of information retrieval and recommendation system.
• Learning and optimization for vaccine selection and personalized/stratified medicine.
• Data-driven surrogate-assisted evolutionary algorithms in pharmaceutical manufacturing processes. 
• Modeling and prediction in epidemic surveillance system for disease prevention. 
• Route planning for disability robots.

Important Dates

• Paper submission: 7th January, 2019
• Notification to authors: 7th March, 2019
• Final submission: 31st March, 2019
• Early registration: 31st March, 2019

Organized by Handing Wang (wanghanding.patch@gmail.com), Rong Qu, Yaochu Jin

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: 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 Special Session on Evolutionary Computer Vision and Image Processing (Jan 15th)

http://homepages.ecs.vuw.ac.nz/~alsahahari/cec19_ss_ecvip.html

Vision is the complex process of deriving meaning from what is seen. The fields of computer vision and image processing have tried to automate tasks that the human visual system can do, with the aim of gaining a high-level understanding of images and videos. Computer vision algorithms have been successfully applied to a large number of real-world problems ranging from remote sensing to medical image analysis, video surveillance, human-robot interaction, and computer-aided design. In turn, evolutionary computation methods have been shown to be more efficient than classical optimization approaches for discontinuous, non-differentiable, multimodal and noisy problems. They have also demonstrated their ability as robust approaches to cope with the fundamental steps of the computer vision and image processing pipeline (e.g. restoration, segmentation, registration, or tracking). As a result of the convergence of the computer vision and evolutionary computation research fields, a large number of research activities have arisen in the last two decades.


Scope and Topics

The proposed special session aims to bring together theories and applications of evolutionary computation techniques to computer vision and image processing problems. In this sense, this special session aims to be a meeting place for researchers in the fields of computer vision and/or evolutionary computation, with the aim of enriching both disciplines by means of the hybridization of state-of-the-art approaches from those domains. Topics of interest include, but are not limited to:

• New theories and methods in the application of evolutionary computation paradigms to computer vision and image processing problems.
• Evolutionary computation paradigms include
• genetic algorithms,
• genetic programming,
• evolutionary strategies,
• evolutionary programming,
• particle swarm optimization,
• ant colony optimization,
• and differential evolution, among many others (including single- and multi-objective optimization algorithms).
• Potential applications in computer vision and image processing include
• image segmentation
• image registration
• image restoration
• image feature extraction
• visual scene analysis
• object detection and classification
• handwritten digit recognition
• object tracking
• face detection and identification
• texture image analysis
• human activity recognition
• robot vision
• and 3D scene reconstruction, among many others.
• Given the huge impact of deep learning in the computer vision community, especially from 2012, and the astonishing performance provided by deep learning algorithms in computer vision tasks, cross-fertilization of evolutionary computation and deep or shallow neural networks applied to vision tasks is especially encouraged. This includes research in transfer learning and domain adaptation, as well as new training strategies based on evolutionary computation techniques, and any hybridization of evolutionary computation with
• multi-layer perceptrons,
• autoencoders,
• adversarial networks,
• Boltzmann machines,
• Hopfield networks,
• deep belief networks,
• neural Turing machines,
• convolutional neural networks,
• and recurrent neural networks, among many other neural models.
• Hybridizations of evolutionary computation methods and other computational intelligence and machine learning techniques (e.g. fuzzy systems, reinforcement learning, artificial immune systems, and learning classifier systems), applied to computer vision and image processing tasks are also encouraged.

Important Dates

Paper Submission Deadline: 15 Jan 2019

Notification of Acceptance: 15 Mar 2019

Final Paper Submission Deadline: 15 Apr 2019

Paper Submission

Papers for IEEE CEC 2019 should be submitted electronically through the Congress website at http://www.cec2019.org/papers.html#submission, and will be refereed by experts in the fields and ranked based on the criteria of originality, significance, quality and clarity. To submit your papers to the special session, please select the Special Session name in the Main Research topic.

For more submission information please visit: http://cec2019.org/. All accepted papers will be published in the IEEE CEC 2019 electronic proceedings, included in the IEEE Xplore digital library, and indexed by EI Compendex.

Organisers

Pablo Mesejo, School of Computer Science, University of Granada, Spain, pmesejo@decsai.ugr.es

Harith Al-Sahaf, School of Engineering and Computer Science, Victoria University of Wellington, New Zealand, harith.al-sahaf@ecs.vuw.ac.nz

Youssef S.G. Nashed, Argonne National Laboratory, United States, ynashed@anl.gov

Biography of the organisers:

Pablo Mesejo received the M.Sc. and Ph.D. degrees in computer science respectively from University of Coruña (Spain) and University of Parma (Italy), where he was an Early Stage Researcher within the Marie Curie ITN MIBISOC ("Medical Imaging using Bio-inspired and Soft Computing"). He was a post-doctoral researcher at the ALCoV team of University of Auvergne (France) and the Mistis team of Inria Grenoble Rhône-Alpes (France), before joining the Perception team with an Inria Starting Researcher Position. He currently is a Marie Curie Experienced Researcher at the University of Granada (Spain). He is chair of the IEEE Computational Intelligence Society Task Force on Evolutionary Computer Vision and Image Processing, and co-founder and scientific advisor of Panacea Cooperative Research S.Coop., a recently created SME focused on finding intelligent solutions aimed at solving unmet biomedical needs. His research interests include computer vision, machine learning and computational intelligence techniques mainly applied to biomedical image analysis problems.

Harith Al-Sahaf received the B.Sc. degree in computer science from Baghdad University (Iraq), in 2005. He joined the Victoria University of Wellington (VUW), (New Zealand) in July 2007 where he received his MCompSc and PhD degrees in Computer Science in 2010 and 2017, respectively. In October 2016, he has joined the School of Engineering and Computer Science, VUW as a Post-doctoral Research Fellow and as a full-time lecturer since September 2018. His current research interests include evolutionary computation, particularly genetic programming, computer vision, pattern recognition, evolutionary cybersecurity, machine learning, feature manipulation including feature detection, selection, extraction and construction, transfer learning, domain adaptation, one-shot learning, and image understanding. He is a member of the IEEE CIS ETTC Task Force on Evolutionary Computer Vision and Image Processing, the IEEE CIS ETTC Task Force on Evolutionary Computation for Feature Selection and Construction, the IEEE CIS ISATC Task Force on Evolutionary Deep Learning and Applications, and the IEEE CIS ISATC Intelligent Systems for Cybersecurity.

Youssef Nashed is an assistant computer scientist at the Mathematics and Computer Science Division in Argonne National Laboratory, USA, and a fellow at the Northwestern Argonne Institute for Science and Engineering. He is involved in multiple projects with the Advanced Photon Source to develop High Performance Computing solutions to x-ray image reconstruction problems. He received his PhD degree from the University of Parma, Italy, in 2013, where he focused on real-time detection and classification of patterns in images and videos, employing evolutionary algorithms and methods based on the human visual cortex. He is also the author of various open source software libraries for image reconstruction, GPU-¬based metaheuristics, data analysis and visualization. His research interests are in large-scale scientific data analysis and visualization, real-time image processing, machine learning on parallel and distributed architectures, and image reconstruction algorithmic development.

Program Committee (TBC)

• Stefano Cagnoni (University of Parma, Italy)
• Óscar Cordón (University of Granada, Spain)
• Sergio Damas (University of Granada, Spain)
• Eli David (Bar-Ilan University, Israel)
• Ivanoe De Falco (ICAR-CNR, Italy)
• Antonio Della Cioppa (University of Salerno, Italy)
• Francesco Fontanella (University of Cassino and Southern Lazio, Italy)
• Óscar Ibáñez (Panacea Cooperative Research, Spain)
• Mario Köppen (Kyushu Institute of Technology, Japan)
• Krzysztof Krawiec (Poznan University of Technology, Poland)
• Evelyne Lutton (INRA, France)
• Amir Nakib (University of Paris-Est, France)
• Gustavo Olague (CICESE, Mexico)
• Clara Pizzuti (ICAR-CNR, Italy)
• Kai Qin (Swinburne University of Technology, Australia)
• Alessandra Scotto di Freca (University of Cassino and Southern Lazio, Italy)
• Stephen L. Smith (University of York, UK)
• Andy Song (RMIT University, Australia)
• Yanan Sun (Victoria University of Wellington, New Zealand)
• Bing Xue (Victoria University of Wellington, New Zealand)
• Mengjie Zhang (Victoria University of Wellington, NZ)

CFP: IEEE CEC 2019 Special Session on Games (Jan 7th, 2019)

Call for Papers - Special Session on Games

The 2019 IEEE Congress on Evolutionary Computation (IEEE CEC2019)

Wellington, New Zealand, 10-13 June 2019

http://cec2019.org/programs/special_sessions.html#cec-04

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. 

Topics

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

Please select "CEC-04: Special Session on Games" when submitting your paper.

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

*This special session is organized in association with the IEEE Computational Intelligence Society (CIS) Technical Committee on Games (Game TC).