Keynote Speakers

  Keynote A

Xin Yao (University of Birmingham, UK)

Xin Yao

Short Bio: Xin Yao is a Chair (Professor) of Computer Science and the Director of CERCIA (Centre of Excellence for Research in Computational Intelligence and Applications) at the University of Birmingham, UK. He is an IEEE Fellow and the President (2014-15) of IEEE Computational Intelligence Society (CIS). He 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 won the prestigious Royal Society Wolfson Research Merit Award in 2012 and the IEEE CIS Evolutionary Computation Pioneer Award in 2013. His major research interests include large scale global optimisation using co-operative co-evolution.
Title: Large Scale Global Optimisation through Co-operative Co-evolution
Abstract: Evolutionary optimisation has moved on in recent years from optimising just a few dozens of real-valued variables, although they are still challenging problems. This talk will give a brief overview of some recent efforts towards
large scale global optimisation (LSGO) using co-operative co-evolution. Starting the journey from one of the first efforts in optimising problems with up to 1000 real-valued variables [1], we illustrate new challenges posed by such problems to evolutionary computation approaches and how co-operative co-evolution could be harnessed to address some of those challenges. Then we focus on one of the key issues in LSGO by co-operative co-evolution — automatic grouping of variables into different co-evolving sub-populations. This is actually a generic and important issue of learning and understanding problem characteristics, especially the interactions among variables. In practice, there is a trade-off to be made between the time we spend on learning problem characteristics and the time we spend on optimisation. Learning makes sense only if the learned information helps to speed up the optimisation more than the time spent on learning. Unfortunately, little is known about the best trade-off. Much work has been based on computational studies, from simple random grouping [1], which is very fast, to more sophisticated differential grouping [2], which takes more time in learning. Such grouping methods are not restricted to any particular optimisers used. They can be used in conventional evolutionary algorithms, as well as differential evolution [1,3] and particle swarm optimisation [4]. Similar ideas are applicable to combinatorial optimisation too [5]. This talk will end with a brief discussion of future research directions and how nature inspiration should be considered in problem-solving, e.g., optimisation.
[1] Z. Yang, K. Tang and X. Yao, “Large scale evolutionary optimization using cooperative coevolution,” Information Sciences, 178(15):2985-2999, August 2008.
[2] M. N. Omidvar, X. Li, Y. Mei and X. Yao, “Cooperative Co-evolution with Differential Grouping for Large Scale Optimization,” IEEE Transactions on Evolutionary Computation, 18(3):378-393, June 2014.
[3] Z. Yang, K. Tang and X. Yao, “Scalability of Generalized AdaptiveDifferential Evolution for Large-Scale Continuous Optimization,” Soft Computing, 15(11):2141-2155, November 2011.
[4] X. Li and X. Yao, “Cooperatively Coevolving Particle Swarms for LargeScale Optimization,” IEEE Transactions on Evolutionary Computation, 16(2):210-224, April 2012.
[5] Y. Mei, X. Li and X. Yao, “Cooperative Co-evolution with Route DistanceGrouping for Large-Scale Capacitated Arc Routing Problems,” IEEE Transactions on Evolutionary Computation, 18(3):435-449, June 2014.



Keynote B

Kay Chen Tan (National University of Singapore, Singapore)

Kay Chen Tan

Short Bio: Associate Professor TAN Kay Chen received the B. Eng degree with First Class Honors in Electronics and Electrical Engineering, and the  Ph.D. degree from the University of Glasgow, Scotland, in 1994 and 1997, respectively. He is actively pursuing research in computational and artificial intelligence, with applications to multi-objective optimization, scheduling, automation, data mining, and games.
Dr Tan 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; Review), 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 an Invited Keynote/Plenary speaker for over 40 international conferences. He served in the international program committee for over 100 conferences and involved in the organizing committee for over 50 international conferences, including the General Co-Chair for IEEE Congress on Evolutionary Computation 2007 in Singapore. Dr Tan is the General Co-Chair for IEEE World Congress on Computational Intelligence 2016 in Vancouver, Canada. Dr Tan is currently an elected member of AdCom (2014-2016) and is an IEEE Distinguished Lecturer of IEEE Computational Intelligence Society (2011-2013; 2015-2017).
 Dr Tan is the Editor-in-Chief of IEEE Transactions on Evolutionary Computation. He was the Editor-in-Chief of IEEE Computational Intelligence Magazine (2010-2013). He currently serves as an Associate Editor / Editorial Board member of over 20 international journals, such as IEEE Transactions on Cybernetics, IEEE Transactions on Computational Intelligence and AI in Games, Evolutionary Computation (MIT Press), European Journal of Operational Research, Neural Computing and Applications, Journal of Scheduling, International Journal of Systems Science, etc.
Dr Tan is a Fellow of IEEE. He is the awardee of the 2012 IEEE Computational Intelligence Society (CIS) Outstanding Early Career Award for his contributions to evolutionary computation in multi-objective optimization. He also received the Recognition Award (2008) from the International Network for Engineering Education & Research (iNEER) for his outstanding contributions to engineering education and research. He was felicitated by the International Neural Network Society (INNS) India Regional Chapter (2014) for his outstanding contributions in the field of computational intelligence. He was also a winner of the NUS Outstanding Educator Awards (2004), the Engineering Educator Awards (2002, 2003, 2005, 2014), the Annual Teaching Excellence Awards (2002, 2003, 2004, 2005, 2006), the Honour Roll Awards (2007), and a Fellow of the NUS Teaching Academic (2009-2012).
Title: Evolutionary Multi-objective Optimization and Applications
Abstract: Multi-objective Optimization (MO) 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 the MO problems. This talk will provide an overview of evolutionary computation for solving multi-objective optimization problems. It will then discuss challenges faced in the field and present various solutions and applications of evolutionary computation techniques for solving MO problems in engineering, such as in the area of logistics, design optimization and prognostics.



Keynote C

Enhong Chen (University of Science and Technology of China, China)

Short Bio: Enhong Chen, currently a professor and the vice dean of the School of Computer Science, the vice director of the National Engineering Laboratory for Speech and Language Information Processing of USTC, the National Science Fund for Distinguished Young Scholars of China. His research interests include data mining and machine learning, social network analysis and recommender systems. He has published lots of papers on refereed journals and conferences, including TKDE, TMC,KDD, ICDM, NIPS, CIKM. He was on program committees of numerous conferences including AAAI, IJCAI, KDD, ICDM, SDM. He received the Best Application Paper Award on KDD’08 and Best Research Paper Award on ICDM’11, Best of SDM’15 Award. He is a senior member of the IEEE.
Title: User Modeling Methods for Personalized Recommendation
Abstract: In the era of big data, recommender system has become one of the most effective solutions to dealing with the challenge of information overload and improving the quality of personalized services. In order to recommend the right service or information to the right users, the recommendation algorithms have to understand their users precisely, i.e. extracting the requirements (interests) of users from various aspects by modeling the recorded profiles. Along this line, in this talk, I will first give a brief introduction of some applications and fundamental techniques of user modeling in recommender systems. Then, I will present some of our recent research efforts in cross-disciplinary domains on this direction, including (1) Risk-aware investor modeling for online investment recommendations (Internet Finance); (2) Cognitive modeling of examinees for recommending the right remedy plans to students (Smart Education); (3) Indecisiveness mining of online customers for consumption choice recommendation (E-commerce).




Keynote D

Bryan Wei
Bryan Wei (Tsinghua University, China)
Short Bio: Bryan Wei is an assistant professor of School of Life Sciences at Tsinghua University. Bryan received his B.S. from Peking University and Ph.D. from Hong Kong University of Science and Technology, and did his postdoctoral training at Harvard Medical School. His major research interest is DNA nanotechnology and the related applications.
Title: The secret ingredients of complex DNA nanostructures
Abstract: DNA origami and single-stranded tile are two proven approaches to self-assemble finite-size complex DNA nanostructures. The construction elements appeared in structures from these two methods can also be found in legacy DNA nanotechnology tiles such as double crossover tiles. Inspired by the successful formation of complex structures from DNA origami and SST, the question we want to ask is whether we can use the legacy rigid tiles to build finite size structures with similar complexity to that of DNA origami or SST based structures.