Keynote Speakers:


Keynote 1

Learning in the Model Space
Professor Xin Yao
University of Birmingham
https://www.cs.bham.ac.uk/~xin/

Abstract: It is a great challenge to learn from noisy and high dimensional data streams, especially when the data volume is large and concept drift occurs in the data. This talk first introduces the basic ideas behind the learning-in-the-model-space approach, which carries out learning in a model space instead of the original data space. It then illustrates the application of this approach using case studies in fault diagnosis. Some of the key research issues in this approach, including model distance calculation and the co-learning of model parameters and model distance metrics, will be mentioned. Finally, some future work will be pointed out.

Details of the learning-in-the-model-space approach can be found from:

  1. H. Chen, P. Tino, A. Rodan and X. Yao, "Learning in the Model Space for Cognitive Fault Diagnosis," IEEE Transactions on Neural Networks and Learning Systems, 25(1):124-136, January 2014. DOI: 10.1109/TNNLS.2013.2256797.
  2. H. Chen, F. Tang, P. Tino and X. Yao, "Model-based Kernel for Efficient Time Series Analysis," Proceedings of 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD¡¯13), pages 392-400, Chicago, USA,August 11-14, 2013 DOI: 10.1145/2487575.2487700.
  3. J. Quevedo, H. Chen, M. A. Cuguero, P. Tino, V. Puig, D. Garcia, R. Sarrate and X. Yao, "Combining Learning in Model Space Fault Diagnosis with Data Validation/Reconstruction: Application to the Barcelona Water Network," Engineering Applications of Artificial Intelligence, 30:18-29, April 2014, DOI: 10.1016/j.engappai.2014.01.008.

Speaker Bio: Xin Yao is a Chair (Professor) of Computer Science at the University of Birmingham, UK, since the April Fool's day in 1999. His major research interests include evolutionary computation, ensemble learning, and their applications. He has applied ensemble learning, especially negative correlation learning, to several areas, including class imbalance learning, online/incremental learning, and semi-supervised learning. His work won the 2001 IEEE Donald G. Fink Prize Paper Award, 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist), 2011 IEEE Transactions on Neural Networks Outstanding Paper Award, and many other best paper awards. He received the prestigious Royal Society Wolfson Research Merit Award in 2012 and the IEEE CIS Evolutionary Computation Pioneer Award in 2013.

Keynote 2

Advances in Evolutionary Multiobjective Optimization and Applications
Associate Professor Kay Chen Tan
National University of Singapore
http://vlab.ee.nus.edu.sg/~kctan/

Abstract: Multi-objective optimization is widely found in many fields, such as logistics, economics, engineering, or whenever optimal decisions need to be made in the presence of trade-offs. The problem is challenging because it involves the simultaneous optimization of several conflicting objectives in the Pareto optimal sense and requires researchers to address many issues that are unique to MO problems. This talk will first provide an overview of evolutionary computation for multi-objective optimization (EMO). It will then discuss challenges faced in EMO research and present various EMO algorithms for good optimization performance. The talk will also discuss the application of evolutionary computing techniques for solving engineering problems, such as logistics, design optimization and prognostic applications.

Speaker Bio: Dr. Kay Chen Tan received his B. Eng degree with First Class Honors in Electronics and Electrical Engineering, and his Ph.D. degree from the University of Glasgow, Scotland, in 1994 and 1997, respectively. He is currently an Associate Professor in the Department of Electrical and Computer Engineering, National University of Singapore (NUS), Singapore.

Dr. Tan actively pursues research in the area of computational intelligence, with applications to multi-objective optimization, scheduling, automation, data mining, and games. He has published over 100 journal papers, over 100 papers in conference proceedings, co-authored 5 books including Multiobjective Evolutionary Algorithms and Applications (Springer-Verlag, 2005), Modern Industrial Automation Software Design (John Wiley, 2006; Chinese Edition, 2008), Evolutionary Robotics: From Algorithms to Implementations (World Scientific, 2006), Neural Networks: Computational Models and Applications (Springer-Verlag, 2007), and Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms (Springer-Verlag, 2009), co-edited 4 books including Recent Advances in Simulated Evolution and Learning (World Scientific, 2004), Evolutionary Scheduling (Springer-Verlag, 2007), Multiobjective Memetic Algorithms(Springer-Verlag, 2009), and Design and Control of Intelligent Robotic Systems (Springer-Verlag, 2009).

Dr. Tan has been invited as a Keynote/Plenary speaker for over 40 international conferences in the area of computational intelligence. He is an elected member of AdCom for IEEE Computational Intelligence Society from 2014-2016. He serves as the General Co-Chair for 2016 IEEE World Congress on Computational Intelligence to be held in Vancouver, Canada. He has also served in the international program committee for over 100 conferences and involved in the organizing committee for over 50 international conferences, such as the General Co-Chair for 2007 IEEE Congress on Evolutionary Computation in Singapore etc. He has actively served in various committees of the IEEE Computational Intelligence Society, such as conference committee, publication committee, nomination committee, awards committee etc. He was also a Distinguished Lecturer of the IEEE Computational Intelligence Society from 2011-2013 and served as the Chair of Evolutionary Computation Technical Committee from 2008-2009.

Dr. Tan was the Editor-in-Chief of IEEE Computational Intelligence Magazine from 2010-2013. He currently serves as an Associate Editor / Editorial Board member of over 20 international journals, such as IEEE Transactions on Evolutionary ComputationIEEE Transactions on Cybernetics, IEEE Transactions on Computational Intelligence and AI in Games, Evolutionary Computation (MIT Press), European Journal of Operational Research, Journal of Scheduling, etc.

Dr. Tan is a Fellow of IEEE. He was the awardee of "Outstanding Early Career Award" from the IEEE Computational Intelligence Society in 2012 for his contributions to evolutionary computation in multi-objective optimization. He also received the “Recognition Award” from the International Network for Engineering Education & Research (iNEER) in 2008 for his outstanding contributions to engineering education and research. He was a winner of the NUS Outstanding Educator Awards in 2004, the Engineering Educator Awards (2002, 2003, 2005, 2014), the Annual Teaching Excellence Awards (2002, 2003, 2004, 2005, 2006), the Honour Roll Awards in 2007, and a Fellow of the NUS Teaching Academic from 2009-2012.

Keynote 3

Some thoughts on complexity of real-world problems — evolutionary computation for Real World Application
Emeritus Professor Zbigniew Michalewicz
University of Adelaide
http://www.cs.adelaide.edu.au/~zbyszek

Abstract: It seems that research community in general (and Evolutionary Computation research community in particular) focuses on problems which are not relevant to today's challenges represented by real-word. During the talk I will provide my personal perspective on a few important issues, e.g. What are the practical contributions coming from the theory of Evolutionary Algorithms? Did we manage to close the gap between the theory and practice? How do Evolutionary Algorithms compare with Operation Research methods in real-world applications? Why do so few papers on Evolutionary Algorithms describe real-world applications? For what type of problems are Evolutionary Algorithms "the best" method?

Further, it seems that researchers experiment with benchmark problems that are fundamentally the same as 50 years ago, while the complexity of real-world problems is growing very fast (e.g. due to globalisation). Thus there is a need for new class of benchmark problems that reflect the characteristics of modern real-world problems. We argue that the real-world problems usually consist of two or more sub-problems that interact with each other and this interaction is responsible for the complexity of the real-world problems — while this type of complexity in the current benchmark problems is missing. A new problem, called travelling thief problem, is introduced and discussed, that is a combination of two well-known problems: knapsack problem and travelling salesman problem — as a new challenge for research community.

Speaker Bio: Zbigniew Michalewicz is Emeritus Professor at the School of Computer Science, University of Adelaide. He completed his MSc degree at Technical University of Warsaw in 1974 and he received PhD degree from Institute of Computer Science, Polish Academy of Sciences, in 1981. He holds Doctor of Science (Habilitation) degree in Computer Science from the Polish Academy of Science (1997). In April 2002 he received the title of Professor from the President of Poland, Mr. Alexander Kwasniewski. From 1988 to 2004 he was Professor at University of North Carolina at Charlotte (USA). Zbigniew Michalewicz holds also Professor positions at the Institute of Computer Science, Polish Academy of Sciences, at the Polish-Japanese Institute of Information Technology, and a honorary Professor position at State Key Laboratory of Software Engineering of Wuhan University, China. He is also associated with Structural Complexity Laboratory at Seoul National University, South Korea. Zbigniew Michalewicz is a Fellow of the Australian Computer Society. In 2006 he was appointed a Business Ambassador for the State of South Australia. From 2005 — 2013, Zbigniew Michalewicz was the co-founder and Chief Scientific Officer of SolveIT Software, a supply chain optimisation business he grew from zero to almost 180 employees and $20 million in revenue before selling the business to Schneider Electric. SolveIT Software became the 3rd fastest-growing company in Australia in 2012, as ranked by Deloitte; the company won numerous awards, and counted among its customers some of the largest corporations in the world, including Rio Tinto, BHP Billiton, and Xstrata.

His current research interests are in the field of evolutionary computation. He has published several books, including a monograph "Genetic Algorithms + Data Structures = Evolution Programs" (3 editions, a few translations), and over 250 technical papers in journals and conference proceedings. He was one of the editors-in-chief of the "Handbook of Evolutionary Computation". He was the general chairman of the First IEEE International Conference on Evolutionary Computation held in Orlando, June 1994. He has been an invited speaker of many international conferences and a member of 40 various program committees of international conferences during the last 3 years. He is a current member of the editorial board and/or serves as associate editor on 12 international journals. He also published (together with David B. Fogel) a text on modern heuristic methods ( 2 editions, the second one from 2004), "How to Solve It: Modern Heuristics", which is a standard text on hundreds of universities all over the world (Chinese translation of the book appeared in 2003 and Polish translation appeared in 2006). The newest books include "Winning Credibility: A guide for building a business from rags to riches", "Adaptive Business Intelligence", and "Puzzle-Based Learning: An introduction to critical thinking, mathematics, and problem solving."

In December 2013 he was awarded (by the Polish President) the Order of the Rebirth of Polish Polonia Restituta — the second highest Polish state decoration, awarded for outstanding achievements in the field of education, science, sports, culture, arts, economy, national defence, social activities, the civil service and the development of good relations with other countries.