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Keynote Speakers

Topic 1: State-of-art Many-objective Evolutionary Algorithms for Optimization Professor Gary G. Yen, IEEE Fellow, IET Fellow, Oklahoma State University, School of Electrical and Computer Engineering.

Abstract: Evolutionary computation is the study of biologically motivated computational paradigms which exert novel ideas and inspiration from natural evolution and adaptation. The applications of population-based heuristics in solving multiobjective optimization problems have been receiving a growing attention. To search for a family of Pareto optimal solutions based on nature-inspiring problem solving paradigms,Evolutionary Multiobjective Optimization Algorithms have been successfully exploited to solve optimization problems in which the fitness measures and even constraints are uncertain and changed over time.

   When encounter optimization problems with many objectives, nearly all designs performs poorly because of loss of selection pressure in fitness evaluation solely based upon Pareto optimality principle. This talk will survey recently published literature along this line of research- evolutionary algorithm for many-objective optimization and its real-world applications. In particular, focus will be placed on the design of selection strategy, including mating selection and environmental selection. We will show the design of a coordinated selection strategy to improve the performance of evolutionary algorithms in many-objective optimization. This selection strategy considers three crucial factors: 1) the new mating selection criterion considers both the quality of each selected parent and the effectiveness of the combination of selected parents; 2) the new environmental selection criterion directly focuses on the performance of the whole population rather than single individual alone, and 3) both selection strategies are complement to each other and the coordination between them in the evolutionary process can achieve a better performance than each of them used individually. Based on performance metrics ensemble, we will provide a comprehensive measure among all competitors and more importantly reveal insight pertaining to specific problem characteristics that the underlying evolutionary algorithm could perform the best.

Biography: Gary G. Yen received the Ph.D. degree in electrical and computer engineering from the University of Notre Dame in 1992. He is currently a Regents Professor in the School of Electrical and Computer Engineering, Oklahoma State University. His research interest includes intelligent control, computational intelligence, evolutionary multiobjective optimization, conditional health monitoring, signal processing and their industrial/defense applications.

   Gary was an associate editor of the IEEE Transactions on Neural Networks and IEEE Control Systems Magazine during 1994-1999, and of the IEEE Transactions on Control Systems Technology, IEEE Transactions on Systems, Man and Cybernetics and IFAC Journal on Automatica and Mechatronics during 2000-2010. He is currently serving as an associate editor for the IEEE Transactions on Evolutionary Computation and IEEE Transactions on Cybernetics. Gary served as Vice President for the Technical Activities, IEEE Computational Intelligence Society in 2004-2005 and is the founding editor-in-chief of the IEEE Computational Intelligence Magazine, 2006-2009. He was the President of the IEEE Computational Intelligence Society in 2010-2011 and is elected as a Distinguished Lecturer for the term 2012-2014. He received Regents Distinguished Research Award from OSU in 2009, 2011 Andrew P Sage Best Transactions Paper award from IEEE Systems, Man and Cybernetics Society, 2013 Meritorious Service award from IEEE Computational Intelligence Society and 2014 Lockheed Martin Aeronautics Excellence Teaching award. Currently he serves as the chair of IEEE/CIS Fellow Committee and General Co-Chair of 2016 IEEE World Congress on Computational Intelligence to be held in Vancouver, Canada. He is a Fellow of IEEE and IET.

Topic 2: Self-Learning Control of Nonlinear Systems based on Iterative Adaptive Dynamic Programming Approach Professor Derong Liu, IEEE Fellow, Fellow of the International Neural Network Society, University of Science and Technology, Beijing.

Abstract: The optimal control of nonlinear systems often requires solving the nonlinear Hamilton-Jacobi-Bellman (HJB) equation instead of the Riccati equation as in the linear case. The discrete-time HJB (DTHJB) equation is more difficult to work with than the Riccati equation because it involves solving nonlinear partial difference equations. Though dynamic programming has been a useful computational technique in solving optimal control problems for many years, it is often computationally untenable to run it to obtain the optimal solution, due to the backward numerical process required for its solutions, i.e., the well-known "curse of dimensionality". A self-learning control scheme for unknown nonlinear discrete-time systems is developed for this purpose. An iterative adaptive dynamic programming algorithm via globalized dual heuristic programming technique is developed to obtain the optimal controller with convergence analysis. Neural networks are used as parametric structures to facilitate the implementation of the iterative algorithm, which will approximate at each iteration the cost function, the optimal control law, and the unknown nonlinear system, respectively. Simulation examples are provided to verify the effectiveness of the present self-learning control approach.

Biography: Derong Liu received the Ph.D. degree in electrical engineering from the University of Notre Dame in 1994. He was a Staff Fellow with General Motors Research and Development Center, from 1993 to 1995. He was an Assistant Professor with the Department of Electrical and Computer Engineering, Stevens Institute of Technology, from 1995 to 1999. He joined the University of Illinois at Chicago in 1999, and became a Full Professor of Electrical and Computer Engineering and of Computer Science in 2006. He was selected for the “100 Talents Program” by the Chinese Academy of Sciences in 2008, and he served as the Associate Director of The State Key Laboratory of Management and Control for Complex Systems at the Institute of Automation, from 2010 to 2015. He is now a Full Professor with the School of Automation and Electrical Engineering, University of Science and Technology Beijing. He has published 17 books (seven research monographs and ten edited volumes). He is an elected AdCom member of the IEEE Computational Intelligence Society, and he is the Editor-in-Chief of Artificial Intelligence Review (Springer). He was the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems from 2010 t0 2015. He was the General Chair of 2014 IEEE World Congress on Computational Intelligence and is the General Chair of 2016 World Congress on Intelligent Control and Automation. He received the Faculty Early Career Development Award from the National Science Foundation in 1999, the University Scholar Award from University of Illinois from 2006 to 2009, the Overseas Outstanding Young Scholar Award fr om the National Natural Science Foundation of China in 2008, and the Outstanding Achievement Award from Asia Pacific Neural Network Assembly in 2014. He is a Fellow of the IEEE and a Fellow of the International Neural Network Society.

Topic 3: State-of-art Nonlinear Dynamics, Signal Processing, Graph Theory, and Computational Intelligence for Automated Diagnosis of Neurological Disorders Professor Hojjat Adeli, AAAS Fellow, IEEE Fellow, AIMBE Fellow and American Neurological Association The Ohio State University.

Abstract: In this Keynote Lecture the author presents novel methodologies for automated diagnosis of neurological disorders through adroit integration of nonlinear dynamics, advanced signal processing techniques, graph theory, and computational intelligence. Sample results are presented for diagnosis of the epilepsy, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), Parkinson’s disease, and the Alzheimer’s disease.

Biography: Hojjat Adeli received his Ph.D. from Stanford University in 1976 at the age of 26. He has authored over 550 research and scientific publications in various fields of computer science, engineering, applied mathematics, and medicine including 15 books. In 1998 he received the Distinguished Scholar Award, from The Ohio State University (OSU) “in recognition of extraordinary accomplishment in research and scholarship”. He is the recipient of numerous other awards and honors such as the OSU College of Engineering Lumley Outstanding Research Award (quadruple winner); Peter L. and Clara M. Scott Award for Excellence in Engineering Education, and Charles E. MacQuigg Outstanding Teaching Award, the 2012 IEEE-EMBS Outstanding Paper Award (IEEE Engineering in Medicine and Biology Society), a Special Medal from The Polish Neural Network Society in Recognition of Outstanding Contribution to the Development of Computational Intelligence, Eduardo Renato Caianiello Award for Excellence in Scientific Research from the Italian Society of Neural Networks and an Honorary Doctorate from Vilnius Gediminas Technical University, Lithuania. He is the Founder and Editor-in-Chief of Computer-Aided Civil and Infrastructure Engineering, now in 31st year of publication and Integrated Computer-Aided Engineering, now in 24th year of publication. He is also the Editor-in-Chief of International Journal of Neural Systems. He is a Distinguished Member of ASCE, and a Fellow of AAAS, IEEE, AIMBE, and American Neurological Association.