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Keynote Speakers |
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| Kalyanmoy Deb |
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Deva Raj Chair Professor
Department of Mechanical Engineering
Indian Institute of Technology, Kanpur, India |
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| Evolution's Niche in Applied Optimization Problem Solving |
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| More details ... |
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| Nikhil R. Pal |
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Electronics & Communication Sciences Unit
Indian Statistical Institute, Calcutta, India |
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Fuzzy Rule Based Systems: Advantages, a Few Important Issues, Some Remedies, and Applications |
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Jun Wang |
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Department of Mechanical & Automation Engineering
The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong |
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Neurodynamic Optimization with Its Applications in Robotics and Control |
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| More details ... |
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| Hideyuki Takagi |
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Faculty of Design
Kyushu University, Shiobaru, Minami-ku, Fukuoka, Japan
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| Recent Topics in IEC Research |
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| More details ... |
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| Janusz Kacprzyk |
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Systems Research Institute
Polish Academy of Sciences
Ul. Newelska 6
01-447 Warsaw
Poland |
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Towards Human Inspired Decision Models: Some Clues and Lessons From Neuroeconomics |
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| More details ... |
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| Kalyanmoy Deb |
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Deva Raj Chair Professor, Department of Mechanical Engineering
Indian Institute of Technology, Kanpur, India |
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| Evolution's Niche in Applied Optimization Problem Solving |
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| Abstract: |
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Over the past two decades, evolutionary optimization methodologies have secured their places in the pursuit of solving optimization problems. Evolutionary optimization methodologies are different from usual mathematically oriented optimization methods and mimick natural evolutionary principles in their core. They are flexible to be adapted, work with a set of points in each iteration, employ stochastic search operations, and are extremely parallel algorithms. In this lecture, after briefly describing the working principles of an evolutionary optimization procedure, we shall present a number of different problem scenarios in which evolutionary optimization methodologies have a clear niche over their classical counterparts. The arguments will be supported by appropriate application case studies. |
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Biography:
Kalyanmoy Deb is the Deva Raj Chair Professor of Mechanical Engineering at Indian Institute of Technology Kanpur in India. Currently he is visiting Helsinki of Economics in Finland as a Finland Distinguished Professor. He is the recipient of the prestigious Shanti Swarup Bhatnagar Prize in Engineering Sciences for the year 2005. |
| He has also received the 'Thomson Citation Laureate Award' from Thompson Scientific for having highest number of citations in Computer Science during the past ten years in India. He is a fellow of Indian National Academy of Engineering (INAE), Indian National Academy of Sciences, and International Society of Genetic and Evolutionary Computation (ISGEC). He has received Fredrick Wilhelm Bessel Research award from Alexander von Humboldt Foundation in 2003. His main research interests are in the area of computational optimization, modeling and design, and evolutionary algorithms. He has written two text books on optimization and more than 200 international journal and conference research papers. He has been working in evolutionary optimization for the past 21 years. |
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More information about his research can be found from http://www.iitk.ac.in/kangal/deb.htm. |
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Electronics & Communication Sciences Unit
Indian Statistical Institute, Calcutta, India |
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Fuzzy Rule Based Systems: Advantages, a Few Important Issues,
Some Remedies, and Applications |
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Abstract: |
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The speech will start with a discussion on the desirable attributes that a decision making system should have when it is designed from data. It will be justified that fuzzy systems enjoy some unique properties and can have edges over other modeling tools (but no claim is made that it is an all-cure solution). Yet, often fuzzy modeling is not the tool of choice for non-specialist. Why?
The talk will deal with this issue and identify some of the problems that inhibit non-specialists to exploit fuzzy systems. In this context, a few problems that are not yet solved satisfactorily and possible ways to deal with them will be addressed. One of the major problems relating to data-based design of fuzzy rule based systems is dealing with high dimensional data. This is akin to dimensionality reduction/structure identification and interpretability of the systems. A mechanism to deal with some of these problems in an integrated manner will be discussed. A unique attribute of this approach is that it can exploit subtle non-linear interactions between features, the problem (that we intend to solve), and the tool (that is used to solve the problem). The formulation will be adapted to all three types of fuzzy systems: classification systems, Mamdani-Assilian type function approximation systems and Takagi-Sugeno type function approximation systems. A few applications (eg, in bioinformatics, control and function approximation) of this approach will also be demonstrated. |
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Biography:
Nikhil Ranjan Pal is a Professor of the Electronics & Communication Sciences Unit of Indian Statistical Institute, Kolkata, India. His area of interest encompasses topics such as Bioinformatics, Artificial Neural Networks, Fuzzy Logic, Evolutionary Computing, Machine Learning, Data Mining etc. He is a Fellow of the Institute of Electrical & Electronics |
Engineers, Inc (IEEE) and Indian National Academy of Engineering. He is the Editor-in-Chief of the IEEE Transactions on Fuzzy Systems & an Associate Editor of IEEE Transactions on Systems, Man & Cybernetics (Part B- Cybernetics). He is also associated with many other journals in his area of research. He has published extensively in different areas of computational intelligence & delivered many plenary talks/keynote address'/invited talks in reputed international events across the world. He has co-authored/edited/co-edited 6 books, the latest one being –Advanced Techniques in Knowledge Discovery and Data Mining (Springer Verlag).
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Jun Wang |
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Department of Mechanical & Automation Engineering
The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong |
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Neurodynamic Optimization with Its Applications in Robotics and Control |
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Abstract: |
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Optimization problems arise in a wide variety of scientific and engineering applications. It is computationally challenging when optimization procedures have to be performed in real time to optimize the performance of dynamical systems. For such applications, classical optimization techniques may not be competent due to the problem dimensionality and stringent requirement on computational time. One very promising approach to dynamic optimization is to apply artificial neural networks. Because of the inherent nature of parallel and distributed information processing in neural networks, the convergence rate of the solution process is not decreasing as the size of the problem increases. Neural networks can be implemented physically in designated hardware such as ASICs where optimization is carried out in a truly parallel and distributed manner. This feature is particularly desirable for dynamic optimization in decentralized decision-making situations arising frequently in robotics and control. In this talk, I will present the historic review and the state of the art of neurodynamic optimization models and selected applications in robotics and control. Specifically, starting from the motivation of neurodynamic optimization, we will review various recurrent neural network models for optimization. Theoretical results about the stability and optimality of the neurodynamic optimization models will be given along with illustrative examples and simulation results. It will be shown that many problems in robotics and control systems, such as robot motion planning and model predictive control, can be readily solved by using the neurodynamic optimization models. |
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Biography:
Jun Wang is a Professor and the Director of Computational Intelligence Laboratory in the Department of Mechanical and Automation Engineering at the Chinese University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, and University of North Dakota. Besides, he also holds a Cheung |
Kong Chair Professorship in computer science and engineering at Shanghai Jiao Tong University since 2008. He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology, Dalian, China. He received his Ph.D. degree in systems engineering from Case Western Reserve University, Cleveland, Ohio, USA. His current research interests include neural networks and their applications. He published over 140 journal papers, 11 book chapters, 8 edited books, and numerous conference papers in the areas. He is an Associate Editor of the IEEE Transactions on Neural Networks since 1999 and IEEE Transactions on Systems, Man, and Cybernetics – Part B since 2003, a member of the Editorial Advisory Board of the International Journal of Neural System since 2006. He also served as an Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics – Part C(2002-2005), a guest editor of the special issue of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), and Neurocomputing (2008), He was an organizer of several international conferences such as the General Chair of the 13th International Conference on Neural Information Processing (2006) and the 2008 IEEE World Congress on Computational Intelligence. He served as the President of Asia Pacific Neural Network Assembly in 2006 and as a member of several IEEE technical committees over the years. He is an IEEE Fellow. |
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| Hideyuki Takagi |
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Faculty of Design
Kyushu University, Shiobaru, Minami-ku, Fukuoka, Japan |
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Recent Topics in IEC Research |
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Abstract: |
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The first topic of this talk is to show new types of Interactive
Evolutionary Computation (IEC) application researches. Major IEC
applications are optimizing target systems and creating graphics,
images, shapes, sounds, vibrations, and others. We introduce two new
types of IEC applications. The first one is measuring human characteristics.
IEC is an optimization method based on human subjective evaluation.
Likely reverse engineering, we may measure the evaluation characteristics
or mental conditions of an IEC user by analyzing the outputs from the
target system optimized by the user. The second one is extension of IEC
evaluation. Usually IEC optimizes a target system based on IEC user's
subjective evaluation, i.e. psychological evaluation. We may extend the
evaluation from psychological one to physiological one. We show the
framework of the extended IEC.
The second topic of this talk is to overview researches that try to
reduce IEC user fatigue and show our latest research in this area.
Several approaches have been proposed to reduce IEC user's fatigue;
some of them are improving input/output interface, accelerating EC
search, allowing human intervention into EC search, estimating human
evaluations, and others. Here, we introduce our latest research and
show our view.
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Biography:
Hideyuki Takagi worked for the Central Research Laboratories of
Panasonic Corporation in 1981 - 1995, and was a visiting researcher
at UC Berkeley in 1991 - 1993 hosted by Prof. L. A. Zadeh. He moved
to Kyushu Institute of Design in 1995 as an Associate Professor and now works for Kyushu University since both |
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He had worked on cooperating neural networks, fuzzy systems, and
genetic algorithms and now is focusing on Humanized Computational
Intelligence with interactive evolutionary computation.
Prof. Takagi has worked for IEEE SMC Society as the Vice-President,
the Chair of Technical Committee on Soft Computing, and an Associate
Editor of IEEE Trans. on SMC-B.
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Systems Research Institute
Polish Academy of Sciences
Ul. Newelska 6
01-447 Warsaw
Poland |
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Towards Human Iinspired Decision Models: Some Clues and Lessons From Neuroeconomics |
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| Abstract: |
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We deal with some new computation paradigms, meant as new type of broadly perceived decision making type models, which are inspired by human behavior, and which depart from models that are traditionally employed and advocated.
We consider both the decision analytic type and game theoretic types of models, and present some examples of experiments which clearly suggest a discrepancy between solutions adopted by humans and obtained by using directly the traditional decision analytic and game theoretic models.
To find possible explanations for such discrepancies resort to neuroeconomics which may be viewed as a new emerging field of research at the crossroads of economics, or – more generally – decision making, and brain research, which is basically about neural mechanisms involved in decision making and their economic relations and connotations. We discuss results of brain investigations which indicate which parts of the brain are activated while performing some decision making related courses of action and provide some explanation about possible causes of discrepancies between the results of formal models and experiments. We take into account knowledge about functions and roles played by those parts of the brain while performing tasks of a specific type, and while dealing with some feelings and emotions. Notably, we emphasize the role of fairness with respect to the player’s proposals and responses, fair share, etc.
We propose some new decision analytic and game theoretic models in which the fairness related elements are explicitly reflected. As an illustration we will show some models of this type which can be used for socio-economic development. |
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Biography:
Dr. Janusz Kacprzyk is Full Professor at the Systems Research Institute, Polish Academy of Sciences in Warsaw, Poland.He has Ph.D. in systems analysis and D.Sc. (habilitation) in computer science, both from the Polish Academy of Sciences.He is the Member, Polish Academy of Sciences, and Foreign Member, Spanish Royal Academy |
of Economic and Financial Sciences (RACEF).
He was visting profesor at many universities in the USA, United Kingdom, Italy and China.
His resreach interests include: soft computing, fuzzy logic and computing with words, intelligent computing, control and optimization, and decision support.
Dr Kacprzyk is the author of 5 books, (co)editor of about 35 volumes, and (co)author of more than 300 papers.
Currently, ke is President of IFSA (International Fuzzy Systems Association), and President of the Polish Society for Operational and Systems Research.
Dr. Kacprzyk is Fellow of IEEE and IFSA, and is recipient of numerous awards, notably: 2006 IEEE CIS Pioneer Award for seminal works on multistage fuzzy control, notably fuzzy dynamic programming, and The Sixth Kaufmann Prize and Gold Medal for pioneering works on the use of soft computing in economics and management.
He is the editor in chief of three Springer’s book series, and is on the editorial boards of ca. 30 journals. |
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See Detail Profile at
http://www.ibspan.waw.pl/~kacprzyk |
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