Dr. Chong is an Associate Professor with the School of Computer Science, University of Nottingham Malaysia Campus. He is a member of the Automated Scheduling, Optimization and Planning Research Group, School of Computer Science, University of Nottingham, UK. He was a Marie Sklodowska-Curie Fellow with the School of Computer Science, University of Birmingham, UK (2016-2018).
Fundamentals of Artificial Intelligence
Introduction to Network Communications
Planning, Search and Artificial Intelligence Programming
Operations Research and Modelling
Advanced Computer Communications
Introduction to Modelling and Optimisation
Simulation for Decision Support
Main research interests include broad areas in computational intelligence such as evolutionary computation and neural networks, machine learning, evolutionary game theory, meta-heuristics… read more
S. Y CHONG, P. TINO, J. HE and X. YAO, 2017. A New Framework for Analysis of Coevolutionary Systems - Directed Graph Representation and Random Walks Evolutionary Computation. (In Press.)
P. TINO, S. Y. CHONG and X. YAO, 2013. Complex Co-evolutionary Dynamics - Structural Stability and Finite Population Effects IEEE Transactions on Evolutionary Computation. 17(2), 155-164 S. Y. CHONG, P. TINO, D. C. KU and X. YAO, 2012. Improving Generalization Performance in Co-evolutionary Learning IEEE Transactions on Evolutionary Computation. 16(1), 70-85
S. Y. CHONG, P. TINO and X. YAO, 2008. Measuring Generalization Performance in Co-evolutionary Learning IEEE Transactions on Evolutionary Computation. 12(4), 479-505
Main research interests include broad areas in computational intelligence such as evolutionary computation and neural networks, machine learning, evolutionary game theory, meta-heuristics (hyper-heuristics, memetic algorithms, etc.), and complex and dynamical systems.
Some ongoing studies in various areas in computational intelligence includes:
1) evolutionary and co-evolutionary learning (their modelling as complex and dynamical systems), 2) evolutionary optimisation (single and multi-objective), 3) representation, selection, and variation operators (e.g., self-adaptation), 4) statistical analysis (e.g., generalization performance measures), 5) diversity maintenance (e.g., niching and speciation), 6) simulation and modeling for (strategic) decision-making processes (e.g., games such as the prisoner's dilemma), 7) evolutionary artificial neural networks.