Located at

Teaching

School of Computer Science and Engineering
Nanyang Technological University
Block N4, 2a-28, Nanyang Avenue, Singapore 639798
Tel 1: +65-6790-5778
Tel 2: +65-6790-6448
Fax: +65-6792-6559
Present Courses

RE2006: Engineering Computation
Renaissance Engineering Programme

Previous Courses Taught

CZ4042-CPE422-CSC422 Artificial Neural Networks (Prescribed Elective)

CSC207 Software Engineering, Year 2 Core

SC206 Microprocessor Systems Design

SC302/CPE302 Computer Networks, Year 3 Core

BI6121 High Performance Computing in Bioinformatics, Master of Science (Bioinformatics)

Yew-Soon Ong

Email To

 

asysong<@>ntu.edu.sg
Fully-funded PhD scholarships with very attractive monthly stipend and allowance are available at SIMTech-NTU Joint Laboratory on Complex Systems in School of Computer Science and Engineering, Nanyang Technological University, Singapore!!!



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 Programme Principal Investigator of the Data Analytics & Complex System Programme in the Rolls-Royce@NTU Corporate Lab. He was Director of the Centre for Computational Intelligence or Computational Intelligence Laboratory from 2008-2015. He received his Bachelors and Masters degrees in Electrical and Electronics Engineering (Specializing in Computing) from Nanyang Technological University and subsequently his PhD (in the area of Artificial Intelligence in Complex Design) from the University of Southampton, United Kingdom.

He 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, International Journal of Systems Science, IEEE Computational Intelligence Magazine (2010 - 2016), and chief editor of Book Series on Studies in Adaptation, Learning, and Optimization as well as Proceedings in Adaptation, Learning, and Optimization He co-edited several special issues in the IEEE Transactions on Evolutionary Computation, IEEE Trans SMC-B, Journal of Genetic Programming and Evolvable Machines, co-edited several books, including Multi-Objective Memetic Algorithms, Evolutionary Computation in Dynamic and Uncertain Environments, and a volume on Advances in Natural Computation published by Springer Verlag. He served as Chair of the IEEE Computational Intelligence Society Emergent Technology Technical Committee (ETTC) from 2011-2012, and as founding chair of the Task Force on Memetic Computing in ETTC since 2006 as well as a member of IEEE CIS Evolutionary Computation Technical Committee from 2008 - 2010. He was also Chair of the IEEE Computational Intelligence Society Intelligent Systems Applications Technical Committee (ISATC) from 2013-2014. He continues to serve actively in the IEEE Computational Intelligence Committee as a member of the IEEE Transactions CIAIG Steering Committee (secretary, 2014 - present), nominations committee (2013 - present) and awards committee.

His current research interests include Computational Intelligence spanning Memetic Computation, Evolutionary Optimization and Machine Learning. His research grants comprises of external funding from both national and international partners that include Boeing Research & Development (USA), Rolls-Royce (UK) and Honda Research Institute Europe (Germany), the National Research Foundation of Singapore, National Grid Office, A*STAR, Singapore Technologies Dynamics and MDA-GAMBIT. His research on Memetic Computation was first featured by Thomson Scientific's Essential Science Indicators as one of the most cited emerging area of research in August 2007. Recently, he is recognized as a Thomson Reuters Highly Cited Researcher in 2015 and 2016 and among the World's Most Influential Scientific Minds. He also received the 2015 IEEE Computational Intelligence Magazine Outstanding Paper Award and the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award for his work in Memetic Computation.

Several of his research technologies in Computational Intelligence and Memetic Computation have been commercialized and licensed to companies and institutions worldwide, where he received the Research Commercialization award in 2015. An overview of his research & technological showcase is available 'Here'. His research in Evolutionary & Memetic Computation has led to the research-enabled IOS 'Dark-Dots Game'. It was the top action game in 48 countries including USA, China and Singapore; downloaded by well over 448,000 players worldwide when launched, with 27% of its players from China and 17% from the USA.

Research Statement: The technology behind the core game mechanic of Dark Dot is the Flocking Animation and Modelling Environment (FAME). FAME is a method in which multiple units or agents react together as a flock or swarm in a consistent manner, based on inputs involving shape, space and time. The research was funded by GAMBIT MDA-NRF with Dr. Ong as the Principal Investigator and is a co-Inventor of the technology. FAME was also commercialized worldwide as a crowd simulation software package through the Unity Asset Store in 2014. More than 130 licenses have been snap-up within two months of its release.

Other successful translations of memetic computing technologies led by him include the: "Algorithm Development Environment for Problem Solving", a Patented system designed for self-configurations of optimizers and "Large-Scale Engineering Simulation for Complex Adaptive Systems (LesCaS)", a decision support system designed for large scale modelling, simulation and optimization of complex systems, which was also licensed to the industry.

In teaching, he has also received a number of awards including the Prestigious Nanyang Education Award (University) in 2016, Nanyang Education Award (College of Engineering) in 2015, Nanyang Excellence Award (School of Computer Science & Engineering) in 2008, Most Popular Lecturer Award 2009. He was featured in the 2014 Nanyang Chronicle for introducing new waves of pedagogical and innovative use of technology to improve teaching, while featured in the Bright Minds Magazine as a qualified faculty that transforms students into promising gems in 2009. In 2012, he was invited as Fellow of Esteemed Renaissance Engineering Programme and as committee member of the Teaching Council at Nanyang Technological University.

IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE CIS, Founding Editor-in-Chief: Yew-Soon Ong



Springer-Verlag Book Series: 'STUDIES IN ADAPTATION, LEARNING, AND OPTIMIZATION', Chief editor, Yew-Soon Ong.



Springer-Verlag Proceeding Series: 'PROCEEDINGS IN ADAPTATION, LEARNING, AND OPTIMIZATION', Chief editor, Yew-Soon Ong.



Memetic Computing Journal, Springer-Verlag, Founding Technical Editors-in-Chief: Yew-Soon Ong



CEC 2017 Competition on Evolutionary Multi-task Optimization, IEEE Congress on Evolutionary Computation, 2017 June 5-8, Donostia - San Sebastian, Spain.



Selected Refereed Publications

EVOLUTIONARY & MEMETIC COMPUTATION (Theory, Algorithms, Survey & Applications)

L. Feng, Y. S. Ong, S. Jiang and A. Gupta, "Autoencoding Evolutionary Search with Learning across Heterogeneous Problems", IEEE Transactions on Evolutionary Computation, In Press, 2017. Available here as PDF file.

J. Zhong, L. Feng and Y. S. Ong, "Gene Expression Programming: A Survey", IEEE Computational Intelligence Magazine, In Press, 2017.

Y. Zeng, X. Chen, Y. S. Ong, J. Tang and Y. Xiang, "Structured Memetic Automation for Online Human-like Social Behavior Learning", IEEE Transactions on Evolutionary Computation, Vol. 21, No. 1, pps. 102-115, 2017. Available here as PDF file.

Y. S. Ong, and A. Gupta, "Evolutionary Multitasking: A Computer Science View of Cognitive Multitasking", Cognitive Computation, Vol. 8, No. 2, pps. 125-142, 2016. Available here as PDF file.

A. Gupta, Y. S. Ong, L. Feng and K. C. Tan, "Multi-Objective Multifactorial Optimization in Evolutionary Multitasking", IEEE Transactions on Cybernetics, Accepted 2016. Available here as PDF file.

A. Gupta, Y. S. Ong, and L. Feng, "Multifactorial Evolution: Towards Evolutionary Multitasking", IEEE Transactions on Evolutionary Computation, vol. 20, no. 3, pp. 343 - 357, 2016. Available here as PDF file.

  • B. Da, Y. S. Ong, L. Feng, A.K. Qin, A. Gupta, Z. Zhu, C. K. Ting, K. Tang, and X. Yao, "Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results", Technical Report, 2016. Available here as PDF file

  • Y. Yuan, Y. S. Ong, L. Feng, A.K. Qin, A. Gupta., B. Da, Q. Zhang, K. C. Tan, Y. Jin, and H. Ishibuchi, "Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results", Technical Report, 2016. Available here as PDF file.
  • "For more info on 'Multifactorial Evolution- Evolutionary Multitasking', Benchmark Problems, Publications and Source Codes Downloads, Click here!"

    L. Feng, Y. S. Ong, A. H. Tan and I. W. Tsang, "Memes as Building Blocks: A Case Study on Evolutionary Optimization + Transfer Learning for Routing Problems", Memetic Computing, vol. 7, no. 3, pp. 159-180, 2015. Available here as PDF file.

    L. Feng, Y. S. Ong, M.-H. Lim, and I. W. Tsang, "Memetic Search with Inter-Domain Learning: A Realization between CVRP and CARP", IEEE Transactions on Evolutionary Computation, vol. 19, no. 5, pp. Oct 2015. Available here as PDF file.

    M. N. Le, Y. S. Ong, Y. Jin and B. Sendhoff, "A Unified Framework for Symbiosis of Evolutionary Mechanisms with Application to Water Clusters Potential Model Design", IEEE Computational Intelligence Magazine, Vol. 7, No. 1, pp. 20 - 35, 2012. *Bestowed the 2015 IEEE CIS Outstanding Computational Intelligence Magazine Paper Award. Available here as PDF file.

    X. S. Chen, Y. S. Ong, M. H. Lim and K. C. Tan, "A Multi-Facet Survey on Memetic Computation", IEEE Transactions on Evolutionary Computation, Vol. 15, No. 5, pp. 591 - 607, Oct 2011. Available here as PDF file.

    Y. S. Ong, M. H. Lim and X. S. Chen, "Research Frontier: Memetic Computation - Past, Present & Future", IEEE Computational Intelligence Magazine, Vol. 5, No. 2, pp. 24 -36, 2010. Available here as PDF file.

    Q. H. Nguyen, Y. S. Ong and M. H. Lim, A Probabilistic Memetic Framework, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 3, pp. 604-623, June 2009. *Bestowed the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award Available here as PDF file or at IEEE Xplore as PDF file. *Source code Download*.

    M. N. Le, Y. S. Ong, Y. Jin & B. Sendhoff, 'Lamarckian memetic algorithms: local optimum and connectivity structure analysis', Memetic Computing , Vol. 1, No. 3, pp. 175-190, 2009. Available here as PDF file. *Source code Download*. 

    Z. Zhu, Y. S. Ong and M. Dash, Wrapper-Filter Feature Selection Algorithm Using A Memetic Framework, IEEE Transactions On Systems, Man and Cybernetics - Part B, vol. 37, no. 1, pp. 70-76, Feb 2007. Available here as PDF file. *Source code Download*.

    Y. S. Ong, M. H. Lim, N. Zhu and K. W. Wong, Classification of Adaptive Memetic Algorithms: A Comparative Study, IEEE Transactions On Systems, Man and Cybernetics - Part B, Vol. 36, No. 1, pp. 141-152, February 2006. Available here as PDF file.

    Y. S. Ong and A.J. Keane, Meta-Lamarckian Learning in Memetic Algorithm, IEEE Transactions On Evolutionary Computation, Vol. 8, No. 2, pp. 99-110, April 2004. *Featured by Thomson Scientific's Essential Science Indicators as one of the most cited papers in August 2007. Available here as PDF file.


    EVOLUTIONARY OPTIMIZATION meets MACHINE LEARNING

    A. Kattan, A. Agapitos,Y. S. Ong, A. A. Alghamedi and M. O'Neill, GP Made Faster with Semantic Surrogate Modelling, Information Sciences, Vol. 355-356, pps. 169-185, 2016.

    J. H. Zhong, Y. S. Ong and W. T. Cai, Self-Learning Gene Expression Programming, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 1, pp. 65-80, 2016.

    A. Kattan and Y. S. Ong, Surrogate Genetic Programming: A Semantic Aware Evolutionary Search, Information Science, Vol. 296, pps. 345-359, 2015.

    M. N. Le, Y. S. Ong, S. Menzel, Y. Jin and B. Sendhoff, Evolution by Adapting Surrogates, Evolutionary Computation Journal, Vol. 1, No. 2, pps. 313-340, 2013. Available here as Available here as PDF file.

    S.D. Handoko, C.K. Kwoh and Y. S. Ong, "Feasibility Structure Modeling: An Effective Chaperon for Constrained Memetic Algorithms", IEEE Transactions on Evolutionary Computation, Vol. 14, No. 5, pp. 740-758, Jun 2010. Available here as PDF file.

    D. Lim, Y. Jin, Y. S. Ong and B. Sendhoff, "Generalizing Surrogate-assisted Evolutionary Computation", IEEE Transactions on Evolutionary Computation, Vol. 14, No. 3, pp. 329-355, Jun 2010. Available here as PDF file. *Source code Download*.

    Y. S. Ong, P. B. Nair and K. Y. Lum, Max-Min Surrogate-Assisted Evolutionary Algorithm for Robust Design, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 4, pp. 392-404, August 2006. Available here as PDF file.

    Z. Z. Zhou, Y. S. Ong, P. B. Nair, A. J. Keane and K. Y. Lum, Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization, IEEE Transactions On Systems, Man and Cybernetics - Part C, Vol. 37, No. 1, Jan. 2007, pp. 66-76. Available here as PDF file.

    Y. S. Ong, P.B. Nair and A.J. Keane, 'Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling', American Institute of Aeronautics and Astronautics Journal, 2003, Vol. 41, No. 4, pp. 687-696. Available here as PDF file. *Source code Download*


    ARTIFICIAL INTELLIGENCE - MACHINE LEARNING

    Y. Zhai, Y. S. Ong, and I. W. Tsang, "Making Trillion Correlations Feasible in Feature Grouping and Selection", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 38, No. 12, pp. 2472-2486, 2016. Available here as PDF file

    I. Chaturvedi, Y. S. Ong, and R. V. Arumugam, "Deep Transfer Learning for Classification of Time-Delayed Gaussian Networks", Signal Processing, Vol. 110, pps. 250-262, 2015.

    Y. Zhai, M. K. Tan, I. W. Tsang and, Y. S. Ong, "Discovering Support and Affiliated Features from Very High Dimensions", International Conference on Machine Learning (ICML 2012), June 2012.

    Y. Zhai, Y. S. Ong, and I. W. Tsang, "The Emerging Big Dimensionality", IEEE Computational Intelligence Magazine, Vol. 9, No. 3, pp. 14-26, 2014. Available here as PDF file.

    C. W. Seah, Y. S. Ong, and I. W. Tsang, "Combating Negative Transfer from Predictive Distribution Differences", IEEE Transactions On Cybernetics, No. 99, pps. 1-13, 2013. Available here as PDF file

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