Friday, 22 June 2018

Call for Participation: IEEE World Congress on Computational Intelligence (IEEE WCCI 2018), Rio de Janeiro, Brazil (Jul 8-13)

The IEEE World Congress on Computational Intelligence (IEEE WCCI) is the largest technical event in the field of computational intelligence. The IEEE WCCI 2018 will host three conferences: The 2018 International Joint Conference on Neural Networks (IJCNN 2018), the 2018 IEEE International Conference on Fuzzy Systems (FUZZ- IEEE 2018), and the 2018 IEEE Congress on Evolutionary Computation (IEEE CEC 2018) under one roof. It encourages cross-fertilization of ideas among the three big areas and provides a forum for intellectuals from all over the world to discuss and present their research findings on computational intelligence.

IEEE WCCI 2018 will be held at the Windsor Barra Convention Centre, Rio de Janeiro, Brazil. Rio de Janeiro is a wonderful and cosmopolitan city, ideal for international meetings. Rio boasts fantastic weather, savory cuisine, hospitable people, and modern infrastructure. Rio is the first city to receive the Certificate of World Heritage for its Cultural Landscape, recently conferred by UNESCO.

IJCNN is the flagship conference of the IEEE Computational Intelligence Society and the International Neural Network Society. It covers a wide range of topics in the field of neural networks, from biological neural network modeling to artificial neural computation.

FUZZ-IEEE is the foremost conference in the field of fuzzy systems. It covers all topics in fuzzy systems, from theory to applications.

IEEE CEC is a major event in the field of evolutionary computation, and covers all topics in evolutionary computation from theory to applications.

The highlights of the Congress include:

Apart from the technical program, participants are also cordially invited to attend various social events that will include welcome reception and conference banquet. In addition, participants are also encouraged to explore the beautiful city of Rio de Janeiro which has an endless supply of attractions and things to see and do (

Wednesday, 20 June 2018

Call for Participation: International Summer Camp on AI, Hefei, China (Jul 1-14)

    Artificial Intelligence (AI) has become an important engine for next ‘Cambrian of Civilization’, burst of knowledge and technology. Do you want to know the state of the arts of AI? Do you want to experience and touch by yourself the most advanced techniques of AI? Do you want to know the trend of AI in both academic and industry? Do you want to know all the above happened in China? Come to join us! This Summer Camp will bring you with excellent experience in both technology and culture. You may get some knowledge about why AI is so special in China, and why AIs in China are so special.
Further Information:

CFP: Australasian Joint Conference on Artificial Intelligence (AI 2018) (Jul 1)

The Australasian Joint Conference on Artificial Intelligence is an annual conference that has dedicated to fostering research communication and collaboration among Australasian AI community since inception. The 31th Australasian Joint Conference on Artificial Intelligence will be hosted by Victoria University of Wellington, New Zealand in December 2018. The Program Committee invites prospective authors to submit original and previously unpublished research and application papers in all spectrums of Artificial Intelligence. The conference topics cover but are not limited to the following areas:
Prospective authors are invited to submit their original unpublished work to AI 2018. The topics of interest to this conference include but are not limited to the following:
  • Agent-based and multiagent systems
  • AI applications and innovations
  • Cognitive modeling and computer human interaction
  • Big data capture, representation, and analytics
  • Commonsense reasoning
  • Computer vision and image processing
  • Constraint satisfaction, search and optimization
  • Data mining and knowledge discovery
  • Evolutionary computation and learning
  • Fuzzy systems and neural networks
  • Game playing and interactive entertainment
  • Intelligent education and tutoring systems
  • Knowledge acquisition and ontologies
  • Knowledge representation and reasoning
  • Machine learning and applications
  • Multidisciplinary AI
  • Natural language processing
  • Planning and scheduling, combinatorial optimization
  • Uncertainty in AI
  • Visualisation in AI
  • Robotics
  • Game theory
  • Text mining and Web mining
  • Web/Social media mining
We encourage cross-boundary works contributing to theory and practice of AI. Novel application domains including cybersecurity, healthcare, IoT, social media and big data real-world applications are highly welcome. Submitted papers should not exceed 12 pages and should not be under review or submitted for publication elsewhere during the review period. All papers will be peer-reviewed by at least three independent referees.
All papers accepted and presented at AI 2018 will be included in the conference proceedings published by the Springer Lecture Notes in Computer Science/Artificial Intelligence (LNCS/LNAI, pending to approval), which are typically indexed by Engineering Index (Compendex), ISI Proceedings/ISTP, and DBLP.

Submission page is here.

Call for applications: IEEE CIS Scientific Mentoring Program 2018/2019

*** Description ***
The IEEE CIS Scientific Mentoring Program  is a service coordinated by the IEEE CIS Neural Network Technical Committee to support the research activity of IEEE CIS student members and young professionals. The Scientific Mentors can help IEEE CIS student members and young professionals by supporting their growth and guiding the steps in the field of Neural Networks and Learning Systems. This is a great opportunity for IEEE CIS student members and young professionals that can find in the Scientific Mentor a source of suggestions and feedbacks about the research.

*** Key aspects of the Scientific Mentoring Program ***
- Fixed time-horizon: 6 months
- Scope: support the research activity in the field of Neural Networks and Learning Systems
- Target: IEEE CIS Student Members and Young Professionals

*** Scientific Mentors for 2018/2019 ***
- Ivo Bukovsky, Professor, Czech Technical University in Prague, Czech Republic
- Catherine Huang, Senior Data Scientist, McAfee, USA
- Wei Lee Woon, Professor, Khalifa University, Abu Dhabi
- Marley Vellasco, Professor, Pontifícia Universidade Católica do Rio de Janeiro, Brasil

*** Procedure for the application ***
The applicant must send by email:
- A motivation letter (max 1 page)
- A short CV (max 1 page)
at the coordinator of the Scientific Mentoring Program for 2018/2019, Prof. Manuel Roveri, ( by the submission deadline. The applicant could also suggest in the motivation letter one or two favorite mentors.

*** Important Dates ***
- Deadline for the submission of the application: September 30, 2018
- Notification:  October 20, 2018
- Mentoring Period: November 2018 - April 2019

*** Contacts ***
For further information about the Scientific Mentoring Program for 2018/2019, please contact Prof. Manuel Roveri, Politecnico di Milano, Italy,

Tuesday, 12 June 2018

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


  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. 


This special issue will present novel results from different subareas 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.


Manuscripts should be prepared according to the “Information for Authors” section of the journal found at pubs/tec/authors/ and submissions should be made through the journal submission website:, 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.


  • 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 Guest Editors.


Pietro S. Oliveto
Department of Computer Science
University of Sheffield
United Kingdom

Anne Auger
Ecole Polytechnique Paris

Francisco Chicano
Department of Languages and Computing Sciences
University of Malaga Spain

Carlos M. Fonseca
Department of Informatics Engineering
University of Coimbra Portugal

Sunday, 10 June 2018

Call for Participation: IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2018), Ottawa, Canada (Jun 12-14)

The IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2018) is dedicated to all aspects of computational intelligence, virtual environments and human-computer interaction technologies for measurement systems and related applications.


Papers are solicited on all aspects of computational intelligence, human-computer interaction technologies, and virtual environments for measurement systems and the related applications, from the points of view of both theory and practice. This includes, but is not limited to, the following topics with specific emphasis on the measurement aspects:
  • Intelligent Measurement Systems
  • Human-computer Interaction
  • Augmented & Virtual Reality
  • Accuracy & Precision of Neural & Fuzzy Components
  • Accuracy & Precision of Virtual Environments
  • Perception, Neurodynamics, Neurophysiology, Psychophysics
  • Multimodal Sensing
  • Multimodal (Visual, Haptic, Audio, etc.) Virtual Environments
  • Sensors & Displays
  • Calibration and System Calibration
  • Multi-Sensor Data Fusion & Intelligent Sensor Fusion
  • Intelligent Monitoring & Control Systems
  • Neural & Fuzzy Technologies For Identification, Prediction, & Control of Complex Dynamic Systems
  • Evolutionary monitoring & control
  • Evolutionary Techniques For Optimization & Logistics
  • Neural & Fuzzy Signal/Image Processing For Industrial, Environmental & Domotic Applications
  • Neural & Fuzzy Signal/Image Processing For Entertainment & Educational Applications
  • Image Understanding & Recognition
  • Machine & Deep Learning for Intelligent Systems
  • Object Modeling
  • Object & System Model Validation
  • Virtual Reality languages
  • Computational Intelligence Technologies For Robotics & Vision
  • Computational Intelligence Technologies For Medical & Bioengineering Applications
  • Computational Intelligence For Entertainment & Educational Applications
  • Distributed Collaborative Virtual Environments
  • Model-Based Telecommunications & Telecontrol Hybrid Systems
  • Fuzzy & Neural Components For Embedded Systems
  • Hardware Implementation of Neural & Fuzzy Systems For Measurements
  • Neural, Fuzzy & Genetic/Evolutionary Algorithms For System Optimization & Calibration
  • Neural & Fuzzy Techniques For System Diagnosis
  • Reliability of Fuzzy & Neural Components
  • Fault Tolerance & Testing In Fuzzy & Neural Components
  • Neural & Fuzzy Techniques For Quality Measurement Standards
  • Human Machine Interaction


Rafik Goubran - Carleton University, Canada

Ana-Maria Cretu - Carleton University, Canada
Dalila Megherbi - University of Massachusetts Lowell, USA

Pierre Payeur -University of Ottawa, Canada
Sebastian Zug - Otto von Guericke University, Germany
Angelo Genovese - Università degli Studi di Milano, Italy

Gabriel Wainer - Carleton University, Canada

Thiago Eustaquio Alves de Oliveira - University of Ottawa, Canada
Ghazal Rouhafzay - Carleton University, Canada

Friday, 8 June 2018

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


  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.


  • 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


D. Zhao, Institute of Automation, Chinese Academy of Sciences, China,

S. Lucas, Queen Mary University of London, UK,

J. Togelius, New York University, USA,


  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
  2. Send also an email to guest editor D. Zhao ( 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.