2018 IEEE World Congress on Computational Intelligence (WCCI 2018)
Rio de Janeiro, BRAZIL, 08-13 July 2018 - http://www.ecomp.poli.br/~wcci2018/
Abstract & Topics
Energy Storage Systems (ESS)s have become widely pervasive in several sectors, both in the civil and in the industrial engineering fields. Among the several applications, the most critical ones regard the storing of energy in the future Smart Grids and microgrids, and the power sourcing for Electric and Hybrid Vehicles. In this context, the management of the ESS represents a crucial task in order to guarantee efficient, effective and robust energy storing. In order to achieve a safe and reliable usage of ESSs, it is important to synthesize suitable models capable to predict the cell behavior in order to avoid damages, to estimate the State of Charge (SoC) and the State of Health (SoH), and to perform the cells equalization. Moreover, the design of efficient and effective algorithms for optimal energy flows routing in Smart Girds and microgrids is a challenging task, especially in presence of ESSs. Computational intelligence techniques represent a powerful approach to face the above-mentioned tasks, allowing to deal with the strong nonlinear and dynamic behavior of electrochemical cells, as well as to design Energy Management Systems (EMS) able to cope with nonlinear and time variant systems, such as microgrids and Smart Grids, especially in presence of stochastic renewable energy sources.
Topics of interest include (but are not limited to) applications of Computational Intelligence techniques (Neural networks and Machine Learning, Evolutionary Optimization and Fuzzy Systems) to the following problems:
- ESS modeling
- ESS parameters identification
- ESS state of charge estimation
- ESS state of health estimation
- ESS cell balancing
- Neural Networks for non-linear system identification
- EMS design for Smart Grids and micro grids in presence of ESSs
- EMS in hybrid and electric vehicles
- EMS in Smart Buildings
- Computational Intelligence techniques for complex systems modeling
Prof. Fabio M. Frattale Mascioli, University of Rome “La Sapienza”, Rome, firstname.lastname@example.org
Prof. Antonello Rizzi, University of Rome “La Sapienza”, Rome, email@example.com
Dr. Maurizio Paschero University of Rome “La Sapienza”, Rome, firstname.lastname@example.org
Fabio Massimo Frattale Mascioli
Prof. Fabio Massimo Frattale Mascioli received his MS and PhD in Information and Communication Engineering in 1989 and 1995, from the University "La Sapienza" of Rome. In 1996, he joined the DIET Department of the University "La Sapienza" of Rome as Assistant Professor. He was promoted to Associate Professor of Circuit Theory in 2000 and to Full Professor in 2011. His research interest mainly regards neural networks and neuro-fuzzy systems and their applications to clustering, classification and function approximation problems, circuit modeling for vibration damping, energy conversion systems and electric and hybrid vehicles. He is author or co-author of more than 150 papers. Since 2007, he serves as scientific director of the `Polo per la Mobilità Sostenibile' (POMOS) Laboratories, DIET Department.
Antonello Rizzi received the Ph.D. in Information and Communication Engineering in 2000, from the University of Rome “La Sapienza”. In September 2000, he joined the INFO-COM Dpt., as an Assistant Professor. Since July 2010 he joined the “Information Engineering, Electronics and Telecommunications” Dpt. (DIET), in the same University. His major research interests are in the area of Soft Computing, Pattern Recognition and Computational Intelligence, including supervised and unsupervised data driven modeling techniques, neural networks, fuzzy systems and evolutionary algorithms. His research activity concerns the design of automatic modeling systems, focusing on classification, clustering, function approximation and prediction problems. Currently, he is working on different research topics and projects, such as Granular Computing, Data Mining and Knowledge Discovery, Content Based Retrieval Systems, classification and clustering systems for structured patterns, graph and sequence matching, agent-based clustering, smart grids and micro-grids modeling and control, intelligent systems for sustainable mobility, battery management systems. Since 2008, he serves as the scientific coordinator and technical director of the R&D activities in the "Intelligent Systems Laboratory" within the Research and Technology Transfer Center for Sustainable Mobility of Lazio Region. He is the scientific coordinator of the "Computational Intelligence and Pervasive Systems" Lab at DIET. Dr. Rizzi (co-)authored more than 140 international journal/conference papers and book chapters. He is a member of IEEE.
Maurizio Paschero is a post-doctoral research associate at the Information Engineering, Electronics and Telecommunications Department of the University of Rome "La Sapienza" since September 2008, where he works in the Polo per la Mobilià Sostenibile (POMOS) Laboratories.
He received his M.S in Electronic Engineering 2003 and the Ph.D in Information and Communication Engineering in 2006 from the University "La Sapienza" of Rome and the Ph.D in Mechanical Engineering in 2008 from Virginia Polytechnic Institute and State University.
His major fields of interest include Soft computing, Smart Grids, multi-physic circuital modeling, intelligent signal processing, and battery modeling. He is author or more than 40 scientific publications on international journals and conferences.
Papers submitted to this Special Session are reviewed according to the same rules as the submissions to the regular sessions of WCCI 2018.
Authors who submit papers to this session are invited to mention it in the form during the submission.
Submissions to Regular and Special Sessions follow identical format, instructions, deadlines and procedures of the other papers.
More information can be found at
Official WCCI 2018 Call for Papers:
Official WCCI 2018 Guidelines: http://www.ecomp.polbr/i.~wcci2018/submissions/#guidelines
The Special Session website:
Paper Submission Deadline: 15 January 2018
Notification of Acceptance: 15 March 2018
Early Registration: 1 May 2018
WCCI 2018 Conference: 8 – 13 July 2018
Thursday, 30 November 2017
Monday, 20 November 2017
Speaker: Prof. Qingfu Zhang, Department of Computer Science, City University of Hong Kong email@example.com
Time: 11am-12pm (GMT), Dec 18, 2017
Multiobjective Evolutionary Computation has been a major research topic in the field of evolutionary computation for many years. It has been generally accepted that combination of evolutionary algorithms and traditional optimization methods should be a next generation multiobjective optimization solver. Decomposition methods have been well used and studied in traditional multiobjective optimization. In this talk, I will describe MOEA/D algorithmic framework. MOEA/D decomposes a multiobjective problem into a number of subtasks, and then solves them in a collaborative manner. MOEA/D provides a very natural bridge between multiobjective evolutionary algorithms and traditional decomposition methods. It has been a commonly used evolutionary algorithmic framework in recent years. I will explain the basic ideas behind MOEA/D and some recent developments. I will also outline some possible research issues in multiobjective evolutionary computation.
Prof. Qingfu Zhang is a Professor at the Department of Computer Science, City University of Hong Kong, Hong Kong. His main research interests include evolutionary computation, optimization, neural networks, data analysis, and their applications. He is currently leading the Metaheuristic Optimization Research Group in City University of Hong Kong. MOEA/D, a multiobjective optimization algorithmic framework, developed in his group, is one of the most widely used and researched multiobjective evolutionary algorithmic framework.
Dr. Zhang is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the IEEE Transactions on Cybernetics. He is also an Editorial Board Member of three other international journals. He was awarded the 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is a 2016 and 2017 highly cited researcher in Computer Science (Clarivate Analytics) and an IEEE fellow. He was selected in the 1000 talents program in China in 2015. He was a Changjiang visiting chair professor with Xidian University, China from 2011 to 2014.
Registration on GoToWebinar: https://attendee.gotowebinar.com/register/672887209381227778
Friday, 17 November 2017
Jul 8-13, 2018, Rio de Janeiro, BRAZIL
We proposed a special session on “Evolutionary Computation in Healthcare Industry” in IEEE IEEE Congress on Evolutionary Computation 2018 (CEC 2018). Please consider to contribute to and/or forward to the appropriate groups the following opportunity to publish original research articles in CEC 2018.
Call for Papers
Summary of the special session:
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. This special session aims to promote the research on evolutionary computation methods for their application to the healthcare industry.
Scope and Topics:
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.
-Paper submission: 15th January, 2018
-Notification to authors: 15th March, 2018
-Final submission: 1st May, 2018
-Early registration: 1st May, 2018
Handing Wang, Department of Computer Science, University of Surrey, UK
Rong Qu, School of Computer Science, University of Nottingham, UK
Dujuan Wang, College of Transportation Management, Dalian Maritime University, China
Yaochu Jin, Department of Computer Science, University of Surrey, UK
Wednesday, 15 November 2017
CFP: WCCI 2018 Special Session: Computational Intelligence for the Automated Design of Machine Learning and Search (CIAD 2018)
Aims, Scope and List of Topics:
Machine learning and search algorithms play an imperative role in solving real world problems in industry and business sectors. Systems employing these techniques have contributed to many facets of industry including data mining, transportation, health systems, computer vision, computer security, robotics, software engineering and scheduling amongst others. These systems employ one or more techniques such as neural networks, fuzzy logic, evolutionary 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 many person-hours. Consequently, there have been a number of initiatives to automate these processes using computational intelligence.
There has been a fair amount of research into parameter tuning and control. The field of auto-ML 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 shown to be effective in the automated design of search techniques. Evolutionary algorithms such as genetic programming and genetic algorithms have made a valuable contribution to this field. The aim of this special session is to examine and promote 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 following:
* Architecture design, e.g. design of neural networks and multi-agent architectures
* Automated hybridization of intelligent techniques
* Automatic programming
* Derivation of constructive heuristics
* Derivation of evaluation functions
* Derivation of operators
* Explainable machine learning
* Parameter control and tuning
* Search-based software engineering
University of Pretoria, South Africa
University of Nottingham, UK
Paper submission deadline: 15 January, 2018
Paper acceptance notification: 15 March, 2018
Final paper submission deadline: 1 May, 2018
Early registration: 1 May, 2018
Special session papers are treated the same as regular papers and must be submitted via the WCCI 2018 submission website. When submitting choose the " Computational Intelligence for the Automated Design of Machine Learning and Search " special session from the "Main Research Topic" list.
Monday, 13 November 2017
Dr. Veronique Ventos, Associate Professor, University Paris Saclay Abstract
Games have always been an excellent field of experimentation for the nascent techniques in computer science and in different areas of Artificial Intelligence including Machine Learning. Despite their complexity, game problems are much easier to understand and to model than real life problems. Systems