Jinghui Zhong

Associate Professor

Room B3-443

School of Computer Science & Engineering

South China University of Technology

Guangzhou Higher Education Mega Centre

510006, Guangzhou

P. R. China

E-mail: jinghuizhong@scut.edu.cn

I am currently an Associate Professor with the School of Computer Science & Engineering, South China University of Technology, China. I received the Ph.D. degree from the School of Information Science and Technology, Sun YAT-SEN Universityin 2012. From 2013 to 2016, I worked as a Research Fellow in the School of Computer Science & Engineering,Nanyang Technological University, Singapore.

Research Interests

Computational Intelligence: Genetic Programming, Differential Evolution, etc.

Machine Learning: Deep Learning, Gaussian Process, etc.

Agent-Based Modeling and Simulation：Crowd Modeling and Simulation, etc.

Publications

Selected Journal Papers

(1)J. Zhong, Y.-S. Ong, and W. Cai, “Self-Learning Gene Expression Programming,” IEEE Transactions on Evolutionary Computation, 2016. 20(1): 65-80.

(2)J. Zhong, M. Shen, J. Zhang, H. H. Chung, Y. H. Shi, and Y. Li, “A Differential Evolution Algorithm with Dual Populations for Solving Periodic Railway Timetable Scheduling Problem,” IEEE Transactions on Evolutionary Computation, 2013, 17(4), pp.512-527.

(3)J. Zhong, F. Liang, and Y.-S. Ong, “Gene expression Programming: A Survey,” IEEE Computational Intelligence Magazine, 2017 (Accepted).

(4)J. Zhang, Z. H. Zhan, Y. Lin, N. Chen, Y. J. Gong, J. Zhong, H. S.H. Chung, Y. Li and Y. H. Shi, “Evolutionary Computation Meets Machine Learning: A Survey,” IEEE Computational Intelligence Magazine, Vol.6, pp.68-75, Nov. 2011.

(5)J. Zhong, W. Cai, L. Luo, and M. Zhao, “Learning behavior patterns from video for agent-based crowd modeling and simulation,” Autonomous Agents and Multi-Agent Systems, 2016, 30(5): 990-1019.

(6)J. Zhong, W. Cai, M. Lees, and L. Luo, “Automatic Model Construction for the Behaviour of Human Crowds”, Applied Soft Computing, 2017 (Accepted)

(7)Y. Xue,J. Zhong (corresponding author), T. H. Tan, Y. Liu, W. Cai, M. Chen, and J. Sun, “IBED: Combining IBEA and DE for Optimal Feature Selection in Software Product Line Engineering,”Applied Soft Computing, 2016, 49：1215-1231.

(8)J. Zhong, and W. Cai, “Differential Evolution with Sensitivity Analysis and the Powell's Method for Crowd Model Calibration,” Journal of Computational Science, 2015, Vol.9, pp. 26-32, 2015.

(9)J. Zhong, N. Hu, W. Cai, M. Lees, and L.B. Luo, “Density-Based Evolutionary Framework for Crowd Model Calibration,” Journal of Computational Science, Vol.6, pp. 11-22, 2015.

(10)L. Luo, H. Yin, W. Cai, J. Zhong, M. Lees, “Design and Evaluation of a Data-driven Scenario Generation Framework for Game-based Training,” IEEE Transactions on Computational Intelligence and AI in Games, 2016, in pressing.

Selected Conference Papers

(1)J. Zhongand W. Cai, “A Hyper-Heuristic Framework for Agent-Based Crowd Modeling and Simulation,” In Proceedings of the 2016 International Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2016), pp.1331-1332, 2016.

(2)M. Zhao, J. Zhong, and W. Cai, “A Role-dependent Data-driven Approach for High Density Crowd Behavior Modeling,” SIGSIM Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS 2016), pp.89-97, 2016.

(3)J. Zhong, W. Cai, L. Luo, and H. Yin, “Learning Behavior Patterns from Video: A Data-driven Framework for Agent-based Crowd Modeling,” In Proceedings of the 2015 International Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2015), pp.801-809,International Foundation for Autonomous Agents and Multiagent Systems, 2015.

(4)J. Zhong, L. Luo, W. Cai, and M. Lees, “Automatic Rule Identification for Agent-Based Crowd Models Through Gene Expression Programming,” In Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2014), pp.1125-1132, International Foundation for Autonomous Agents and Multiagent Systems, 2014.

(5)J. Zhong and J. Zhang, “SDE: A Stochastic Coding Differential Evolution for Global Optimization,” In Proceedings of the 2012 Genetic and evolutionary computation Conference (GECCO2012), pp.975-981, 2012.

(6)J. Zhong and J. Zhang, “Ant Colony Optimization Algorithm for Lifetime Maximization in Wireless Sensor Network with Mobile Sink,” In Proceedings of the 2012 Genetic and evolutionary computation Conference(GECCO 2012), pp. 1199-1204, 2012.

(7)Y. Lin, J. Zhong, and J. Zhang, “Parallel exploitation in estimated basins of attraction: a new derivative-free optimization algorithm,” In Proceedings of the 201 Genetic and evolutionary computation Conference(GECCO 2011), pp. 133-138, 2011.

(8)J. Zhong and J. Zhang, “Energy-efficient local wake-up scheduling in wireless sensor networks,” In Proceedings of the 2011 IEEE Congress on Evolutionary Computation (CEC 2011), pp.2280-2284, 2011.

(9)J. Zhong and J. Zhang, “Adaptive Multi-objective Differential Evolution with Stochastic Coding Strategy,” In Proceedings of the 2011 Genetic and evolutionary computation Conference(GECCO 2011), pp. 665-672, 2011.

(10)J. Zhong, J. Zhang, and Z. Fan, “MP-EDA: A Robust Estimation of Distribution Algorithm with Multiple Probabilistic Models for Global Continuous Optimization”, In Simulated Evolution And Learning 2010 (SEAL 2010), pp. 85-94, 2010.

Projects

(1)A cooperative Coevolutionary Genetic Programming Framework for Crowd Modeling and Simulation, National Natural Science Foundation of China，PI，2017.1-2019.12

(2)Distributed Genetic Programming and its Applications, Fundamental Research Funds for the Central Universities，PI，2017.1-2018.12