Friday 21 December 2018

CFP: IEEE CEC 2019 Special Session on Evolutionary Algorithms for Complex Optimization in the Energy Domain

Keeping the world at current pace in what concerns energy demand is not possible with the limited world resources. Sustainability and efficiency are a crucial part of the process to enable this sustained growth. Moreover, adequate methods of energy optimization are highly relevant in the current paradigm playing a key role in the planning, operation, and control of energy systems. However, several optimization problems in the energy domain are complex by nature since they are highly constrained and face issues related to high-dimensionality, lack of information, noisy and corrupted data as well as real-time requirements. Under these circumstances, achieving good solutions in a reasonable amount of time remains a challenge in most problems. Even the most sophisticated exact solutions require workarounds that often lead to unsatisfactory performance and applicability of the algorithms. Due to the difficulties of traditional algorithms to find feasible solutions for those complex problems in real-world conditions, Evolutionary Computation (EC) has emerged and demonstrated satisfactory performance in a wide variety of applications in the energy domain.

Scope and Topics

This is a follow-up of the previous special session in WCCI2018. Research work is welcome concerning complex real-world applications of EC in the energy domain. The problems can be focused on different parts of the energy chain (e.g., heating, cooling, and electricity supply) and different consumer targets (e.g., residential or industrial level). Problems dealing with uncertainty, dynamic environments, many-objectives, and large-scale search spaces are important for the scope of this special session. This special session aims at bringing together the latest applications of EC to complex optimization problems in the energy domain. Besides, this special session is linked to the competition on “Evolutionary Computation in Uncertain Environments: A Smart Grid Application”. Therefore, participants are also welcome to submit the results of their algorithm to our session.

List of topics

Topics must be related to EC in the energy domain including, but not limited to:

  • Electric and plug-in hybrid vehicles
  • Electricity markets
  • Energy scheduling
  • Heat and electricity joint optimization problems
  • Hydrogen economy problems
  • Multi/many-objective problems in the energy domain
  • Natural gas optimization problems
  • Optimal power flow in distribution and transmission
  • Residential, industrial and district cooling/heating problems
  • Smart grid and micro-grid problems
  • Solar and wind power integration and forecast
  • Super grids problems (continental and trans-continental transmission system) 
  • Transportation & energy joint problems
  • Distributed evolutionary approaches in the energy domain

How to submit a paper

Select our SS name under the main topic in the upload paper section 


  • João Soares, Polytechnic of Porto, PT,
  • Fernando Lezama, Polytechnic of Porto, PT,
  • Zita Vale, Polytechnic of Porto, PT,
  • Markus Wagner, Adelaide University, AU,

Further related bibliography

[1] Joao Soares, Bruno Canizes, M. A. Fotouhi Gazvhini, Zita Vale, and G. K. Venayagamoorthy, “Two-stage Stochastic Model using Benders’ Decomposition for Large-scale Energy Resources Management in Smart grids,” IEEE Transactions on Industry Applications, 2017.

[2] Fernando Lezama, Joao Soares, Enrique Munoz de Cote, L. E. Sucar, and Zita Vale, “Differential Evolution Strategies for Large-Scale Energy Resource Management in Smart Grids,” in GECCO ’17: Genetic and Evolutionary Computation Conference Companion Proceedings, 2017.

[3] João Soares, Mohammad Ali Fotouhi Ghazvini, Marco Silva, Zita Vale, Multi-dimensional signaling method for population-based metaheuristics: Solving the large-scale scheduling problem in smart grids, Swarm and Evolutionary Computation, 2016.

[4] Joao Soares, Hugo Morais, Tiago Sousa, Zita Vale, Pedro faria, Day-ahead resource scheduling including demand response for electric vehicles, IEEE Transactions on Smart Grid 4 (1), 596-605, 2013.

CFP: IEEE CEC 2019 Special Session on Evolutionary Computation for Multi-Agent Systems

Multi-agent systems (MAS) are computerized systems composing of multiple interacting and autonomous agents within a common environment of interest for problem-solving. The development of intelligent agents that are capable of adapting to the complex or dynamic environment has attracted increasing attentions over the past decades. In computational intelligence, evolutionary computation (EC), in particular, has been shown to provide a reliable and flexible contender over traditional mathematical approaches for solving complex optimization problems, especially if near global optimum solutions are sought. According to the recent studies, EC based techniques, including evolutionary algorithms, swarm intelligence, evolutionary reinforcement and transfer learning, are already starting to show up in developing more significant intelligence among multiple agents in MAS. Particularly, the intrinsic parallelism of natural evolution and the errors which are introduced due to the physiological limits of the agents’ ability to perceive differences, could generate the “growth” and “variation” of knowledge that agents have of the world, thus exhibiting high adaptivity capabilities on solving complex and non-trivial problems.
Taking this cue, the present special session aims at bringing together researchers from both academia and industry to explore the recent developments, future directions and the potential challenges in this field. The topics of this special session include but are not limited to the following topics:
  • EC based mission planning for multi-agent systems
  • EC based task allocation for multi-agent systems
  • Co-evolution and the evolution of collaborative behavior of multiple agents
  • Evolutionary reinforcement learning for multi-agent systems
  • Evolutionary transfer learning for multi-agent systems
  • Evolution of neural controllers for self-organized mobile robots
  • Discovery of emergency of swarm agents
  • Swarm intelligence (such as PSO, ACO, etc.) inspired multi-agent systems
  • Memetic automaton and meme-centric multi-agent system
  • EC based human–machine interaction for multi-agent systems
  • Real-world applications of EC based multi-agent systems, e.g. mobile robots (unmanned ground robots, unmanned surface/underwater vehicles, aerial robots, space exploration), search and rescue, virtual game and industrial.


School of Marine Science and Technology
Northwestern Polytechnical University
127 Youyi West Rd. Xi’an, Shaanxi Province, P. R. China,
School of Computer Science and Engineering,
Dalian University of Technology
No.2 Linggong Road, Liaoning Province, P.R.C., 116024
School of Computer Science and Engineering,
Nanyang Technological University
N4-02a-20, Nanyang Avenue, Singapore 639798

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 for Multi-Agent Systems”.

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:
We look forward to receiving your high-quality submissions.

Wednesday 19 December 2018

CFP: IEEE CEC 2019 Special Session on Swarm Intelligence in Operations Research, Management Science, and Decision-Making (Jan 7th)

Swarm intelligence, as a crucial aspect of the artificial intelligence domain, has become an increasingly important modern computational intelligence method in artificial intelligence and computer science. In swarm intelligence, the nascent collective intelligence of groups of simple agents possess a powerful global search capability, and has been demonstrated to be able to determine the optimal solution within a rational time by numerous study fields using swarm intelligence algorithms, such as GA, MA, ACO, PSO, ABC, SSO, etc. Swarm intelligence algorithms play a paramount role in optimizing the increasing problems in related complex systems. Despite a significant amount of research on Swarm Intelligence, there remain many open issues and intriguing challenges in the field. This special session will provide a cardinal opportunity to present the latest scientific results and methods on the collaboration of Swarm Intelligence in Operations Research, Management Science and Decision Making, to discuss and exchange the latest developments in Swarm Intelligence, and to explore the future directions in Swarm Intelligence. Authors are invited to submit their original and unpublished work in the areas including, but not limited to:

  • Swarm intelligence in operations research, management science or decision-making
  • Advanced models of swarm intelligence
  • Data mining using swarm intelligence
  • Analytical studies that enhance our understanding of the swarm intelligence behaviors
  • Swarm intelligence optimization techniques
  • Knowledge incorporation in swarm intelligence
  • Network design and routing
  • Multi-objective optimization
  • Industrial engineering optimization problems
  • Computer engineering optimization problems
  • Power and energy design problems
  • Optimization under uncertainty
  • Manufacturing design
  • Reliability design problems
  • Wireless sensor networks

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, 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: 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.

Organized by Wei-Chang Yeh, Zhifeng Hao, and Chun-Cheng Lin
Supported by IEEE CIS ISATC Task Force on Intelligent Adaptive Fault Tolerant Control, Reliability, and Optimization

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

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, 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: 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.


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)

Friday 14 December 2018

CFP: IEEE CEC 2019 Special Session on Evolutionary Computation for Creativity, Manufacture and Engineering Management in the Industry 4.0 Era

Computational intelligence has found increasing applications to smart design, digital manufacturing and engineering management. This Special Session gathers research results and researchers together in this area, with a foresight on Industry 4.0 (i4). Focusing on intelligent manufacturing and cyber-physical systems so far, efforts in either i4 have lacked smart design and business elements for manufacture that are necessary in completing this unprecedented upgrade of the value chain. A rising demand for an intelligent design and manufacturing system, using intelligent tools, such as evolutionary computation, has seen increased requirements of customisation, flexibility, efficiency, responsiveness, and cost-effectiveness of products and their manufacturing. These requirements become urgent in the international race to the next "industrial revolution", as highlighted by i4, which aims at upgrading the entire manufacturing value chain through turning the factory floor into an innovation centre capable of mass customisation at a mass production cost.

Scope and Topics

This Special Session solicits original research papers or reviews that would shape and advance design, manufacture and engineering management in the Industry 4.0 era. Computational intelligence utilises a set of nature-inspired modelling and optimisation approaches to complex real-world problems. Papers addressing how to create designs and build machines smartly, thereby leading to a step improvement in manufacturing autonomy and industrial efficiency, performance and competitiveness, would be particularly welcome. 

Main Topics (include, but are not limited to):

  • Computational intelligence automated design 
  • AI-driven engineering design and product creation 
  • AI-guided simulation and modelling 
  • High performance computing for smart design 
  • Cyber-physical systems with computational intelligence
  • Industry 4.0 with computational intelligence
  • Computation intelligence for Industry 4.0 in a cloud and big data environment
  • Computation intelligence and data science for marketing in an Industry 4.0 value chain
  • Evolutionary learning techniques for Industry 4.0 business informatics
  • Computation intelligence and data science applications to marketing for design;
  • Evolutionary distributed or cloud computing for interactive product design and marketing;
  • High-volume industrial products for industry 4.0 customisation and innovation
  • Intelligent machinery and smart manufacturing
  • Computational intelligence for sensor technology in Industry 4.0
  • Evolutionary computation for scheduling, layout and process optimisation in Industry 4.0
  • Multi-objective optimisations for design and manufacture
  • Hardware and software platforms for smart design automation  
  • Intelligent manufacturing system design, optimisation, control and operation
  • Evolutionary computation in industrial computer vision and robotics
  • Human-machine interface and integration in an industrial environment
  • Robotics-related computer hardware, software, and architectures
  • Robotics in manufacturing and flexible automation
  • Autonomous design and manufacturing
  • Intelligent operational research and management

Special Session Organisers

(1)Dr Leo Yi Chen,

Industry 4.0 Artificial Intelligence Laboratory, Dongguan University of Technology

Leo Yi Chen (BSc, MSc, BSc, MSc, PhD, CEng, FHEA, SMIEEE, MIMEchE, MAAAI, MIET, MAIAA, MASME), is with Industry 4.0 Artificial Intelligence Laboratory at Dongguan University of Technology. His research which has been published in scholarly journals and edited volumes, explores the aspects: artificial intelligence, intelligent design and manufacture, robotics, etc. Dr Chen also studies advanced simulations of evaluation and preference in multidisciplinary contexts via high performance computing, which demonstrates significant research and grant potential in engineering and cross-disciplinary applications. Besides, He is one of the Editorial Board Members, and he has been one of the Guest Editors for 5 special issues. He is supervising 3 PhD students and has examined over 20 Postgraduate students' theses as both internal and external examiner. Dr Chen has published over 70 academic papers in both high impact international academic journal and international conferences and has been selected as one of the Publons' top 1% of reviewers in Computer Science and Engineering. He has been actively involved in both academic research and KTP projects as PI and CoI funded by the EPSRC (UK), Horizon2020 (EU), NSFC (China) and Industrial funding bodies. One of the co-organisers of the WCCI'16: Computational Intelligence for Industry 4.0 Special Session.

(2)Professor Chuan-Kang Ting,

Department of Power Mechanical Engineering, National Tsing Hua University

Chuan-Kang Ting received the B.S. degree from National Chiao Tung University, Taiwan, the M.S. degree from National Tsing Hua University, Taiwan, and the Ph.D. degree from the University of Paderborn, Germany. He is currently a Professor at Department of Power Mechanical Engineering, National Tsing Hua University, Taiwan. His research interests are in evolutionary computation, computational intelligence, metaheuristic algorithms, and their applications in intelligent machinery and manufacturing, transportation and logistics networks, bioinformatics, music and games. He is an Associate Editor of IEEE Transactions on Emerging Topics in Computational Intelligence, and an Editorial Board Member of Soft Computing and Memetic Computing journals. He chaired the AI Forum 2012 and co-chaired the 2013 IEEE Symposium on Computational Intelligence for Creativity and Affective Computing.

(3)Professor Hongnian Yu,

Industry 4.0 Artificial Intelligence Laboratory, Dongguan University of Technology

Faculty of Science and Technology, Bournemouth University

Hongnian Yu has held academic positions at the Universities of Sussex, Liverpool John Moor, Exeter, Bradford, Staffordshire and Bournemouth in the UK. He is currently Professor in Computing at Bournemouth University. He has extensive research experience in mobile computing, modelling, scheduling, planning, and simulations of large discrete event dynamic systems with applications to manufacturing systems, supply chains, transportation networks, computer networks and RFID applications, modelling and control of robots and mechatronics, and neural networks. He has graduated over 20 PhD/MPhil and MRes research students, is supervising 8 PhD students, and has examined over 20 PhD/MPhil students’ theses as both internal and external examiner. He has published over 200 journal and conference research papers. He has held several research grants from the UK EPSRC, the Royal Society, and the EU, AWM, as well as from industry. Prof Yu was awarded the F.C. William Premium for his paper on adaptive and robust control of robot manipulators by the IEE Council.He is a member of the EPSRC Peer Review College. Prof Yu has a strong research collaboration with partners from China, France, Germany, Hungary, Italy, Japan and Thailand.

(4)Professor Yun Li,

Industry 4.0 Artificial Intelligence Laboratory, Dongguan University of Technology

Department of Design, Manufacture and Engineering Management, University of Strathclyde

Yun Li worked as Consultant Engineer at U.K. National Engineering Laboratory in 1989, and as post-doctoral research engineer at U.K. Industrial Systems and Control Ltd in 1990. In 1991, he joined University of Glasgow as Lecturer and developed one of the world’s first 30 evolutionary computation courses in 1995 with the popular online interactive genetic algorithm courseware EA Demo.  In 1998, he established and chaired both the IEEE Computer-Aided Control System Design Evolutionary Computation Working Group, and the European Network of Excellence in Evolutionary Computing Workgroup on Systems, Control, and Drives for Industry.  During 2011-2013, Prof Li served as the Founding Director of University of Glasgow Singapore.  Professor Li has over 200 publications, one of which is in “Research Front in Computer Science”, one in “Research Front in Engineering”, four in “Essential Science Indicators”, one the ‘most popular’ every month in IEEE Transactions on Control Systems Technology and another ‘most cited’ in IEEE Transactions on Systems, Man, and Cybernetics – Part B. Prof Li organized an Industry 4.0 Special Session at ICAC’15 and at WCCI'16, and chaired the UK EPSRC funded conference “Looking Beyond Industry 4.0” held in Glasgow in 2017.

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.

CFP: IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob 2019) (22 Feb)

ICDL-EpiRob is a unique conference gathering researchers from computer science, robotics, psychology and developmental studies to share knowledge and research on how humans and animals develop sensing, reasoning and actions. This includes taking advantage of interaction with social and physical environments and how cognitive and developmental capabilities can be transferred to computing systems and robotics. This approach goes hand in hand with the goals of both understanding human and animal development and applying this knowledge to improve future intelligent technology, including for robots that will be in close interaction with humans.
In this ninth edition of the conference we invite submissions that explore, extend, and consolidate the interdisciplinary boundaries of this exciting research field.


Topics of interest include, but are not limited to:
  • principles and theories of development and learning;
  • development of skills in biological systems and robots;
  • nature vs nurture, developmental stages;
  • models on the contributions of interaction to learning;
  • non-verbal and multi-modal interaction;
  • models on active learning;
  • architectures for lifelong learning;
  • emergence of body and affordance perception;
  • analysis and modelling of human motion and state;
  • models for prediction, planning and problem solving;
  • models of human-human and human-robot interaction;
  • emergence of verbal and non-verbal communication;
  • epistemological foundations and philosophical issues;
  • robot prototyping of human and animal skills;
  • ethics and trust in computational intelligence and robotics;
  • social learning in humans, animals, and robots.


Accepted full six-page paper submissions will be included in the conference proceedings published by IEEE. These will be selected for either oral or poster presentation.
Regular papers can be tagged “MODELbot” to be considered for the MODELbot challenge award, see details here.
Other submission types will be announced through our full call for contributions.


We invite experts in different areas to organise either a tutorial or a workshop to be held on the first day of the conference. Tutorials will provide insights into specific topics through hands-on training and interactive experiences. Workshops will extend to a half or full day and could include invited speakers, discussions with a smaller community, and independent submissions.


  • Tutorial/workshop submission deadline: October 30, 2018
  • Paper submission deadline: February 22, 2019
  • Author notification: May 1, 2019
  • Camera ready due: June 1, 2019
  • Conference: August 19–22nd 2019


  • General chairs: Jim Torresen, Kerstin Dautenhahn
  • Program chairs: Kai Olav Ellefsen, Katharina J. Rohlfing
  • Finance / Website Chairs: Kyrre Glette, Charles Martin
  • Publicity chairs: Ryo Kurazume, Bruno Castro da Silva, Kazi Shah Nawaz Ripon
  • Bridge chair: Tetsuya Ogata, Emre UgurLocal chairs: Bruno Laeng, Tor Endestad

Monday 10 December 2018

CFP: IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr 2019) (8 Dec)

EEE CIFEr, Computational Intelligence for Financial Engineering and Economics is the major collaboration between the professional engineering and financial communities, and is one of the leading forums for new technologies and applications in the intersection of computational intelligence and financial engineering and economics with a history starting from 1990s.

We deeply believe that besides inviting our prestigious academic scholars, the CIFEr'19 focusing on the theme of powering financial industries with AI will be unique and special in its connecting international computational intelligence communities with many booming Fintech companies with AI innovative applications and more than 8,000 hedge funds with AUM Ten Trillions of Chinese Yuan in Shenzhen and other cities in China.

Submission Guidelines

All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:

  • Papers should present academic or practical value, and have not been published in any academic journals or conferences previously.
  • Posters on Fintech innovative applications and Financial Engineering & Economics Applications are warmly welcome.
  • Instructions for Authors:

Scopes (included but not limited to)

AI and Big Data Technologies

  • S1 Deep Learning and Reinforcement Learning
  • S2 Financial Machine Learning and Data Mining
  • S3 Natural Language Processing and Text Ming
  • S4 Probabilistic Modeling/Inference
  • S5 Fuzzy Sets, Rough Sets, & Granular Computing   
  • S6 Evolutionary Computation
  • S7 Intelligent Trading Agents
  • S8 Time Series Analysis
  • S9 Non-linear Dynamics
  • S10 Financial Data Mining
  • S11 Sentiment Analysis and Emotion Mining
  • S12 Alternative Data Integration and Mining
  • S13 Predictive Modeling and Forecasting

Financial Engineering & Economics Applications

  • S14 Algorithmic and Quantitative Trading
  • S15 Portfolio Optimization and Asset Allocation
  • S16 Risk Management
  • S17 Pricing of Structured Securities
  • S18 Hedging Strategies
  • S19 Risk Arbitrage
  • S20 Behavioral Finance
  • S21 Innovative Derivative and Fix Income Products
  • S22 Agent-based Computational Economics
  • S23 Artificial and Emerging Markets
  • S24 Operations Research and Management Sciences
  • S25 Front/Back Office Operations
  • S26 Fintech Innovation and Block Chain Applications

General Co-Chairs

Hisao Ishibuchi (Southern University of Science and Technology)
Dongbin Zhao  (Institute of Automation,Chinese Academy of Sciences)

Organizing committee

Program Co-Chairs

  • Chenghui Cai (Shenzhen WeAI Tech LLC)
  • Wen Dou (Maoyuan Capital LLC, CTIA)
  • Aaron Gong (CME Group)
  • ZongweiLuo (Southern University of Science and Technology)

Industry Liaisons

  • Robert Golan (DB Mind)

Conference Committee

  • David Quintana (Universidad Carlos III de Madrid)
  • Edward Tsang (University of Essex)
  • Michael C S Wong (City University of Hong Kong)
  • Philip Yu (University of Hong Kong)
  • William M.Y. Cheung (Waseda University)
  • Okan Duru (Nanyang Technological University)
  • Aparna Gupta(Rensselaer Polytechnic Institute)
  • Vincent Tam (University of Hong Kong)
  • Jeff(Jun) Nie (Keywise Capital Management, ARIMAC)


IEEE CIS Webinar on Theories for Modelling Reasoning (19th Dec)

Webinar Speaker: Professor María Daniela López De Luise

Webinar Chair: Keeley Crockett
Webinar Title:  Theories for Modelling Reasoning
Date and Time: December 19th, 2018. 13.00 (GMT) and 09:00 in Argentina
Abstract: There are many approaches to model the different abilities of the brain. In this short webinar, three of them will be briefly introduced: Morphosyntactic Linguistic Wavelets, Fuzzy Harmonic Systems, and Bacteria Reasoning. Some of their current and potential applications will also be presented
Webinar ID:  705-024-091

Biography: Professor María Daniela López De Luise has a master’s degree in System Analysis (Buenos Aires University, 1989), Expert System Engineering (INSTITUTO TECNOLOGICO DE BUENOS AIRES, 2000), Ph.D. Computer Sciences (Universidad Nacional de La Plata, 2008). Director, founder, and researcher at CI2S Labs, director of IDTI Lab (UADER, Entre Ríos Argentina), director of graduated Career in Computer Sciences, Lecturer for the local IEEE Lecturer Program, founder and vice-chair of the IEEE CIS Argentina, director of the IEEE GTC Argentina, and IEEE WCI. Declared Eminent Engineer of Region 9 (IEEE). Among other prizes: Banco Rio, Sadosky, CIITI, TRIC. Currently researching in the heuristic prospection of natural language production.

Sunday 2 December 2018

CFP: IEEE CEC Special Session on Smart Logistics (Jan 7)

Call for Papers - Special Session on Smart Logistics
The 2019 IEEE Congress on Evolutionary Computation (IEEE CEC2019)
Wellington, New Zealand, 10-13 June 2019

Important Dates

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

Please select "CEC-44: Special Session on Smart Logistics" when submitting your paper.

Smart logistics refers to the efficient and effective design, planning and control of the supply chain processes though intelligent technologies, such as software to improve the design of networks, software to automate scheduling, routing, and dispatching, material handling systems, etc. Respectively, the relevant research methods involve clustering, stochastic (dual) dynamic programming, planning and optimization. In recent years, evolutionary computation (EC) techniques have been introduced to the area of logistics. Examples include applying single-objective and multi-objective evolutionary algorithms to facility layout decision problems and vehicle routing problems.


This special session aims at presenting the latest research on EC applications to logistics. Real-world applications of EC on logistics are highly recommended. The topics include but are not limited to:
  • facility (re-)layout decision problems
  • routing problems
  • emergency logistics
  • reverse logistics
  • crowd logistics
  • freight transportation
  • green supply chain
  • metropolitan/city logistics
  • uncertainty modelling in planning and control
  • large-scale evolutionary optimization to logistics
  • multi-agent system in logistics
  • internet of things on smart logistics.
Jialin Liu, liujl(at)
Research Assistant Professor, Dept. of Computer Science and Engineering, Southern University of Science and Technology, China
Lecturer in Computer Science, School of Engineering and Computer Science, Victoria University of Wellington, New Zealand
Shengxiang Yang,
Professor, School of Computer Science and Informatics, De Montfort University, UK

Saturday 1 December 2018

CFP: IEEE CEC Special Session on: “Differential Evolution: Past, Present and Future” (Jan 7)

2019 IEEE Congress on Evolutionary Computation (CEC 2019)June 10-13, 2019, Wellington, New Zealand
Special Session on: “Differential Evolution: Past, Present and Future”

Aim and Scope
Differential evolution (DE) emerged as a simple and powerful population-based stochastic optimizer more than two decades ago and has now developed into one of the most promising research areas in the field of evolutionary computation. The successes of DE have been ubiquitously evidenced in a variety of problem domains. Furthermore, the remarkable efficacy of DE in real-world applications has significantly boosted its popularity.
Over the past decades, numerous studies on DE have been carried out to improve the performance of DE, to give a theoretical explanation of the behavior of DE, and to apply DE and its derivatives to solve various scientific and engineering problems, as demonstrated by a huge number of research publications on DE in the forms of monographs, edited volumes and archival articles. Consequently, DE-related algorithms have frequently demonstrated superior performance in challenging tasks. It is worth noting that DE has always been one of the top performers in previous competitions held at the IEEE CEC. Nonetheless, the lack of systematic benchmarking of the DE-related algorithms in different problem domains, the existence of many open problems in DE, and the emergence of new application areas call for an in-depth investigation of DE.
This special session aims at bringing together researchers and practitioners to review and re-analyze past achievements, to report and discuss latest advances, and to explore and propose future directions in this research area. Authors are invited to submit their original and unpublished work in the areas including but not limited to:
·      New DE variants for continuous, discrete, mixed-variable, single-objective, multi-objective, many-objective, constrained, large-scale, multiple optima seeking (niching), dynamic and uncertain optimization
·      Review, comparison and analysis of DE in different problem domains
·      Experimental design and empirical analysis of DE
·      DE-variants for handling mixed-integer, discrete, and binary optimization problems
·      Study on initialization, reproduction and selection strategies in DE
·      Study on control parameters (e.g., scale factor, crossover rate, and population size) in DE
·      Self-adaptive and tuning-free DE
·      Parallel and distributed DE
·      Theoretical analysis and understanding of DE
·      Synergy of DE with neuro-fuzzy and machine learning techniques
·      DE for expensive optimization problems
·      Hybridization of DE with other optimization techniques
·      Interactive DE
·      DE for deep learning
·      Application of DE to real-world problems

Important Dates
·      Paper submission deadline:                 January 7, 2019
·      Paper acceptance notification date:     March 7, 2019
·      Final paper submission deadline:         March 31, 2019
Please refer to for the latest information.

Paper Submission
All papers should be submitted electronically through:
When you submit your papers to our special session, please select "56" as the Main Research Topic*.

Special Session Co-Chairs
Kai Qin
Swinburne University of Technology, Australia
Swagatam Das
Electronics and Communication Sciences Unit, Indian Statistical Institute, India
Rammohan Mallipeddi
School of Electronics Engineering, Kyungpook National University, Daegu, South Korea