计算智能：遗传编程算法、差分算法等

机器学习：高斯过程、EM算法、深度学习等

多智能体建模与仿真：数据驱动的人群行为建模等

 (带*的作者为对应论文的通讯作者)

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

[2] D. Li, J. Zhong*, “Dimensionally Aware Multi-objective Genetic Programming for Automatic Crowd Behavior Modeling,” ACM Transactions on Modeling and Computer Simulation, 2020, Accepted.

[3] S. Huang, J. Zhong*, and W, Yu, “Surrogate-Assisted Evolutionary Framework with Adaptive Knowledge Transfer for Multi-task Optimization,” IEEE Transactions on Emerging Topics in Computing, 2019, Accepted.

[4] Y. Chen, J. Zhong*, L. Feng, and J. Zhang, “An Adaptive Archive-based Evolutionary Framework for Many-task Optimization,” IEEE Transactions on Emerging Topics in Computational Intelligence, 2019, Accepted.

[5] C. Lu, J. Zhong*, Y. Xue, L. Feng, and J. Zhang, “Ant Colony System with Sorting-based Local Search for Coverage-based Test Case Prioritization”, IEEE Transactions on Reliability, 2019, Accepted.

[6] Q. Xiao, J. Zhong*, L. Feng, L. Luo, J. Lv, “A Cooperative Coevolution Hyper-Heuristic Framework for Workflow Scheduling Problem”, IEEE Transactions on Services Computing, 2019, Accepted.

[7]  J. Zhong, L. Feng, W. Cai, and Y.-S. Ong, “Multifactorial Genetic Programming for Symbolic Regression Problems,” IEEE Transactions on Systems, Man, And Cybernetics: Systems, In Press 2018.

[8] 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, Vol. 17, No. 4, pp.512-527, August 2013.

[9]  J. Zhong, L. Feng, and Yew-Soon, Ong, “Gene Expression Programming: A Survey,” IEEE Computational Intelligence Magazine, 12(3):54-72, 2017

[10] T. Wei, J. Zhong*, and J. Zhang, “An Energy-efficient Partition-based Framework with Continuous Ant Colony Optimization for Target Tracking in Mobile Sensor Networks”, IEEE Transactions on Emerging Topics in Computational Intelligence, 2019, Accepted.

[11]    J. Zhong, Z. Huang, L. Feng, W. Du, and Y. Li, “A Hyper-Heuristic Framework for Lifetime Maximization in Wireless Sensor Networks With A Mobile Sink,” IEEE/CAA Journal of Automatica Sinica, 2019, Accepted.

[12]    J. Ji, W. Yu, J. Zhong, and J. Zhang, “Density-Enhanced Multiobjective Evolutionary Approach for Power Economic Dispatch Problems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, Accepted.

[13]    L. Feng, L. Zhou, J. Zhong, A. Gupta, Y-S. Ong, K.C. Tan and A. K. Qin, “Evolutionary Multitasking via Explicit Autoencoding,” IEEE Transactions on Cybernetics, In Press 2018

[14]    M. Zhao, J. Zhong, and W. Cai, “A Role-dependent Data-driven Approach for High Density Crowd Behavior Modeling,” ACM Transactions on Modeling and Computer Simulation, 28(4): 1-25, 2018.

[15]    J. Zhong, Wentong Cai, Linbo Luo, Mingbi Zhao, “Learning behavior patterns from video for agent-based crowd modeling and simulation,” Autonomous Agents and Multi-Agent Systems, Vol.30, No.5, pp.990-1019, 2016.

欢迎有兴趣的学生报考研究生，联系方式: jinghuizhong at scut.edu.cn.