The 11th International Conference on Simulated Evolution and Learning
November 10-13, 2017, Shenzhen, China

SEAL2017 Confirmed Keynote Speakers

  • Prof. Kenneth De Jong:
    Krasnow Institute, George Mason University, Fairfax, Virginia, 22030, USA,
  • Prof. Sanaz Mostaghim:
    Intelligent Systems Group, Faculty of Computer Science, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany,
  • Prof. Yew Soon Ong:
    School of Computer Science and Engineering, Nanyang Technological University, Block N4, 2a-28, Nanyang Avenue, Singapore 639798,
  • Prof. Philip C. L. Chen:
    Faculty of Science and Technology, University of Macau, Macau, China
  • Prof. Jun Wang:
    Department of Computer Science, City University of Hong Kong, Hong Kong, China
  • Prof. Hisao Ishibuchi:
    Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
  • Prof. Yiuming Cheung:
    Department of Computer Science, Hong Kong Baptist University, Hong Kong ymc@Comp.HKBU.Edu.HK

Prof. Kenneth De Jong

Title of talk: Co-evolutionary Algorithms: Theory and Practice
Kenneth A. De Jong received his Ph.D. in computer science from the University of Michigan in 1975. He joined George Mason University in 1984 and is currently a Professor Emeritus of Computer Science, head of the Evolutionary Computation Laboratory, and Associate Director of the Krasnow Institute. His research interests include genetic algorithms, evolutionary computation, machine learning, and complex adaptive systems. He is currently involved in research projects involving the development of new evolutionary algorithm (EA) theory, the use of EAs as high-performance optimization techniques, and the application of EAs to the problem of learning task programs in domains such as robot navigation and game playing. He is an active member of the Evolutionary Computation research community and has been involved in organizing many of the workshops and conferences in this area. He is the founding editor-in-chief of the journal Evolutionary Computation (MIT Press), and a member of the board of ACM SIGEVO. He is the recipient of an IEEE Pioneer award in the field of Evolutionary Computation and a lifetime achievement award from the Evolutionary Programming Society.

Prof. Sanaz Mostaghim

Title of talk: Multi-Objective Optimiztaion and Decision Making in Dynamic Environments
This talk is about multi-objective optimization and decision making algorithms in industrial applications. The talk will give an overview about the design issues for multi-objective optimization algorithms and the challenges in real-time applications such as in robotics and computer games. In most of such applications, the decision maker must find and select one possible optimal solution in a very limited time frame. This is very challenging, when the environment dynamically changes as the decision maker needs to re-optimize and decide on the fly. Multi-objective decision making algorithms in dynamically changing environments will be addressed and applications in computer games and robotics using flying robots will be presented.
Sanaz Mostaghim is a professor of computer science at the Otto von Guericke University Magdeburg, Germany. She holds a PhD degree (2004) in electrical engineering from the University of Paderborn, Germany. Sanaz has worked as a postdoctoral fellow at ETH Zurich in Switzerland (2004-2006) and as a lecturer at Karlsruhe Institute of Technology (KIT), Germany (2006-2013), where she received her habilitation degree in applied computer science in 2012. Her research interests are in the area of evolutionary multi-objective optimization, swarm intelligence, and applications in robotics and science. Sanaz is an active member of IEEE Computational Intelligence Society (CIS) and serves as a member of the CIS Administration Committee (AdCom). She is associate editor of IEEE Transactions on Evolutionary Computation, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Transactions on Cybernetics, IEEE Transactions on Systems, Man and Cybernetics (Systems) and member of the editorial board of springer Journal on Complex and Intelligent Systems.

Prof. Yew Soon Ong

Title of talk: “Why restrict to one task or problem? From Transfer to Multitask Optimization”
Yew-Soon Ong is Professor and Chair of the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He is Director of the Data Science and Artificial Intelligence Research Center, Director of the A*Star SIMTECH-NTU Joint Lab on Complex Systems and Principal Investigator of the Data Analytics & Complex System Programme in the Rolls-Royce@NTU Corporate Lab. He received his PhD from University of Southampton, UK.
Dr. Ong is founding Editor-In-Chief of the IEEE Transactions on Emerging Topics in Computational Intelligence, founding Technical Editor-In-Chief of Memetic Computing Journal (Springer), Associate Editor of IEEE Transactions on Evolutionary Computation, IEEE Transactions on Neural Network & Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Big Data, and others. His research interests in computational intelligence span across memetic computation, evolutionary optimization using approximation/surrogate/meta-models, intelligent agents, machine learning and Data Analytics. His research grant comprises of external funding from both national and international partners that exceed 15 Million USD. Dr. Ong’s research has advanced the academic standing of evolutionary computation, earning him the recognition of a Thomson Reuters Highly Cited Researcher for two consecutive years (2015 and 2016) and a position among the World's Most-Influential-Scientific Minds. He received the 2015 IEEE Computational Intelligence Magazine Outstanding Paper Award and the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award for his work pertaining to Memetic Computation.

Prof. Philip C. L. Chen

Title of talk: Broad Learning System (宽度学习): An effective and efficient incremental learning system without the need for deep architecture
In recent years, deep learning caves out a research wave in machine learning. With outstanding performance, more and more applications of deep learning in pattern recognition, image recognition, speech recognition, and video processing have been developed. The talk is to introduce “Broad Learning”-- a very fast and accurate learning without the need of deep structure. Without stacking the layer-structure, the designed neural networks expand the neural nodes broadly and update the weights of the neural networks incrementally when additional nodes are needed and when the input data entering to the neural networks continuously. The designed network structure and learning algorithm are perfectly suitable for modeling and learning big data environment. Experiments results in MNIST and handwriting recognition and NORB database indicate that the proposed BLS significantly outperforms existing deep structures in learning accuracy and generalization ability.
Dr. Chen is currently the Dean of the Faculty of Science and Technology, University of Macau, Macau, China and a Chair Professor of the Department of Computer and Information Science since 2010. He worked at U.S. for 23 years as a tenured professor, a department head and associate dean in two different universities.
Dr. Chen’s research areas are in systems, cybernetics and computational intelligence. He is a Fellow of the IEEE and AAAS. He was the President of IEEE Systems, Man, and Cybernetics Society (SMCS) (2012-2013). Currently, he is the Editor-in-Chief of IEEE Transactions on Systems, Man, and Cybernetics: Systems (2014-). He has been an Associate Editor of many IEEE Transactions, and currently he is an Associate Editor of IEEE Trans on Fuzzy Systems, IEEE Trans on Cybernetics, and IEEE/CAA Automatica Sinica. He is the Chair of TC 9.1 Economic and Business Systems of IFAC. He is also a Fellow of CAA and Fellow of HKIE and an Academician of International Academy of Systems and Cybernetics Science (IASCYS). In addition, he is an ABET (Accreditation Board of Engineering and Technology Education, USA) Program Evaluator for Computer Engineering, Electrical Engineering, and Software Engineering programs.
Dr. Chen he received Outstanding Electrical and Computer Engineering Award in 2016 from his alma mater, Purdue University, West Lafayette, where he received his Ph.D. degree in 1988, after he received his M.S. degree in electrical engineering from the University of Michigan, Ann Arbor, in 1985.

Prof. Jun Wang

Title of talk: Neurodynamic approaches to distributed, global, and multi-objective optimization

Prof. Hisao Ishibuchi

Title of talk: Evolutionary Many-Objective Optimization and Performance Evaluation
This talk starts with brief introduction to evolutionary multi-objective optimization. Next we discuss difficulties in several aspects related to evolutionary many-objective optimization such as the search for Pareto optimal solutions, the approximation of the Pareto front, the visualization of obtained non-dominated solutions, and the selection of a single final solution. After that, we discuss difficulties of fair performance comparison of EMO (evolutionary multi-objective optimization) algorithms especially for man-objective problems. It is demonstrated that hypervolume-based performance comparison results strongly depend on the choice of a reference point. Only when the shape of the Pareto front is triangular, the effect of the reference point specification is limited to m extreme points of the Pareto front of an m-objective optimization problem. Then we discuss how to specify a reference point for fair performance comparison. This discussion is related to the reference point specification in hypervolume-based EMO algorithms. Finally we discuss difficulties of performance comparison based on the IGD (inverted generational distance) indicator.
Dr. Ishibuchi received the BS and MS degrees from Kyoto University in 1985 and 1987, respectively. In 1992, he received the Ph. D. degree from Osaka Prefecture University where he has been a professor since 1999. From April 2017, he is with Department of Computer Science and Engineering, SUSTech, China. He received a Best Paper Award from GECCO 2004, HIS-NCEI 2006, FUZZ-IEEE 2009, WAC 2010, SCIS & ISIS 2010, FUZZ-IEEE 2011, ACIIDS 2015 and GECCO 2017. He also received a 2007 JSPS Prize. He was the IEEE CIS Vice-President for Technical Activities (2010-2013). Currently, he is the President of the Japan EC Society (2016-2018), the Editor-in-Chief of IEEE CI Magazine (2014-2019) and Journal of Japan EC Society (2014-2018), an IEEE CIS AdCom member (2014-2019), and an IEEE CIS Distinguished Lecturer (2015-2017). He is also an Associate Editor of IEEE TEVC (2007-2017), IEEE Access (2013-2017) and IEEE TCyb (2013-2017). He is an IEEE Fellow.

Prof. Yiuming Cheung

Title of talk: Class Imbalance Learning -- Problem, Modelling and Challenges
In many practical problems, number of data form difference classes can be quite imbalanced, which could make the performance of the most machine learning methods become deteriorate to a certain degree. As far as we know, the problem of learning from imbalanced data continues to be one of the challenges in the field of data engineering and machine learning, which has attracted growing attentions in recent years. In this talk, we will first formally describe the class imbalance problem and its significance with examples from real world applications, and review the existing solutions. Then, three research problems, i.e. sampling strategy, classifier weights of boosting and imbalanced streaming data with concept drift, are studied. Accordingly, we have proposed a solution for each problem. The first solution, namely Hybrid Sampling with Bagging (HSBagging) method, utilizes a new hybrid scheme of undersampling and oversampling with sampling rate selection. This method features both of undersampling and oversampling, and the specifically selected sampling rate for each data set. The second solution is called G-mean Optimized Boosting (GOBoost), which is a boosting framework where the classifier weights are optimized on geometric mean measurement. GOBoost is an ensemble framework that can be applied to any boosting-based method for class imbalance learning by simply replacing the classifier weights updating module. The last solution is called Dynamic Weighted Majority for Imbalance Learning (DWMIL). It creates a base classifier for each chunk and weighs them according to their performance evaluated on the current chunk. Thus, a classifier trained recently or on the similar concept to the current chunk will receive high weight in the ensemble to help prediction. Finally, some challenging problems in this topic are explored as well.
Yiuming Cheung received Ph.D. degree from Department of Computer Science and Engineering at The Chinese University of Hong Kong in 2000. He joined the Department of Computer Science in Hong Kong Baptist University (HKBU) in 2001, and became a full professor since 2012. He is an IET/IEE Fellow, British Computer Society (BCS) Fellow and IETI Fellow, as well as the recipient of “Chu Tian Scholars” in China. His research interests include machine learning, pattern recognition, image and video processing, and optimization. He has published over 200 articles in the high-quality conferences and journals, including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Information Forensics and Security, IEEE Transactions on Image Processing, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on SMC (Part B), Pattern Recognition, and so on. Moreover, he has been granted three invention patents. In recognition of his innovative work, he has been awarded two most prestigious prizes: (1) the Gold Medal with Distinction (i.e. the highest grade in Gold Medals) and (2) Swiss Automobile Club Prize, in the 45th International Exhibition of Invention, Geneva, Switzerland, on March 29-April 2, 2017, which were selected from 1000 new inventions and products of 700+ competition teams from 40 countries. Furthermore, he was the Gold Award Winner of Hong Kong Innovative Invention Award in the Seventh Hong Kong Innovative Technologies Achievement Award 2017. In addition, he was the recipient of the Best Research Paper Award in IEEE IWDVT’2015 and ICNC-FSKD’2014, respectively, and the recipient of 2011 Best Research Award in Department of Computer Science, HKBU. He is the Founding and Past Chairman of IEEE (Hong Kong) Chapter of Computational Intelligence Society, and the Vice-Chair of IEEE Computer Society Technical Committee on Intelligent Informatics. Furthermore, he has taken the key positions in several international conferences, e.g. Area Chair of ICDM’2017, Program Committee Chair of WI’2012 and IAT’2012, Organizing Committee Chair of WI’06, IAT’06, ICDM’06, and IDEAL'2003. He has served as the Guest Editor / Associate Editor in several prestigious international journals, including: IEEE Transactions on Neural Networks and Learning Systems, Pattern Recognition, and Knowledge and Information Systems: An International Journal.