“*” indicates corresponding author.

**2019**

- 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*, 2019, Accepted.**Computing** - 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”,, 2019, Accepted.*IEEE Transactions on Emerging Topics in Computational Intelligence* - 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”,, 2019, Accepted.*IEEE Transactions on Reliability* - Q. Xiao,
**J. Zhong***, L. Feng, L. Luo, J. Lv, “A Cooperative Coevolution Hyper-Heuristic Framework for Workflow Scheduling Problem,”, 2019, Accepted.*IEEE Transactions on Services Computing* - Y. Chen,
**J. Zhong***, L. Feng, and J. Zhang, “An Adaptive Archive-based Evolutionary Framework for Many-task Optimization,”, 2019, Accepted.*IEEE Transactions on Emerging Topics in Computational Intelligence* - J. Ji, W. Yu,
**J. Zhong**, and J. Zhang, “Density-Enhanced Multiobjective Evolutionary Approach for Power Economic Dispatch Problems,”, 2019, Accepted.**IEEE Transactions on Systems, Man, and Cybernetics: Systems** - F. Liang, L, Zhou, A. Gupta,
**J. Zhong**, Z. Zhu, KC. Tan, K. Qin, “Solving Generalized Vehicle Routing Problem With Occasional Drivers via Evolutionary Multitasking ,”, 2019, Accepted.**IEEE Transactions on Cybernetics** **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,”, 2019, Accepted.*IEEE/CAA Journal of Automatica Sinica*- Z. Huang,
**J. Zhong***, L. Feng, Y. Mei, and W. Cai, “ A Fast Parallel Genetic Programming Framework with Adaptively Weighted Primitives For Symbolic Regression ,”, 2019， Accepted.**Soft Computing** - Y. Li, Y. Chen,
**J. Zhong***, and Z. Huang, “Niching Particle Swarm Optimization with Equilibrium Factor for Multi-modal Optimization,”, 2019, Accepted*Information Sciences* - Z. Huang, C. Lu, and
**J. Zhong**, “A Multi-Objective Hyper-Heuristic for Unmanned Aerial Vehicle Data Collection in Wireless Sensor Networks”,, Accepted.**SSCI 2019** **J. Zhong*,**L. Li, W. Liu, L. Feng, and X. Hu, “A Co-evolutionary Cartesian Genetic Programming with Adaptive Knowledge Transfer,”, Accepted*CEC2019*- Q. Shang, L. Zhang, L. Feng, Y. Hou,
**J. Zhong**, A. Gupta, K. C. Tan and H. Liu, “A Preliminary Study of Adaptive Task Selection in Explicit Evolutionary Many-Tasking,”, Accepted*CEC2019*

**2018**

**J.Zhong,**L. Feng, W. Cai, and Y.-S. Ong,**“**Multifactorial Genetic Programming for Symbolic Regression Problems,”, In Press 2018.**IEEE Transactions on Systems, Man, And Cybernetics: Systems**- L. Feng, L. Zhou,
**J. Zhong**, A. Gupta, Y-S. Ong, K.C. Tan and A. K. Qin, “Evolutionary Multi-tasking via Explicit Autoencoding,”, In Press 2018**IEEE Transactions on Cybernetics** - M. Zhao,
**J. Zhong,**and W. Cai, “A Role-dependent Data-driven Approach for High Density Crowd Behavior Modeling,”, 28(4): 1-25, 2018.*ACM Transactions on Modeling and Computer Simulation* - N. Hu,
**J. Zhong**, et al. “*Guide them through: an automatic crowd control framework using multi-objective genetic programming*,”, Volume 66, Pages 90-103, 2018.*Applied Soft Computing* -
**J. Zhong***, Y. Lin, C. Lu, and Z. Huang, “A Deep Learning Assisted Gene Expression Programming Framework for Symbolic Regression Problems”，(7) 2018:530-541 .*ICONIP* - Y. Chen,
**J. Zhong***, “A Fast Memetic Multi-objective Differential Evolution for Multi-tasking Optimization”,, Rio de Janeiro, 2018, pp. 1-8. doi: 10.1109/CEC.2018.8477722 .*2018 IEEE Congress on Evolutionary Computation (CEC)* - D. Liu, S. Huang,
**J. Zhong***, “Surrogate-assisted Multi-tasking Memetic Algorithm”,**2018**, Rio de Janeiro, 2018, pp. 1-8. doi: 10.1109/CEC.2018.8477830.*IEEE Congress on Evolutionary Computation (CEC)* - S. Huang,
**J. Zhong***, “ES-GP: An Effective Evolutionary Regression Framework with Gaussian Process and Adaptive Segmentation Strategy”,. Springer, Cham, 2018: 736-743.*International Conference on Computational Science (ICCS)* - Y. Chen,
**J. Zhong***, M. Tan, “Comprehensive Learning Gene Expression Programming for Automatic Implicit Equation Discovery, ”. Springer, Cham, 2018: 114-128.*International Conference on Computational Science (ICCS)*

**2017**

**J. Zhong**, W. Cai, M. Lees, L. Luo, “Automatic Model Construction for the Behaviour of Human Crowds,”, Vol.56, pp.368-378, July, 2017.*Applied Soft Computing***J. Zhong**, L. Feng, and Yew-Soon, Ong, “Gene Expression Programming: A Survey,”, 12(3):54-72, 2017*IEEE Computational Intelligence Magazine*- L. Luo, X. Hou,
**J. Zhong**, et al. “Sampling-based adaptive bounding evolutionary algorithm for continuous optimization problems,”, Vol.382, pp.216-233, 2017.*Information Sciences* - L. Luo, H. Yin, W. Cai,
**J. Zhong,**M. Lees, “Design and Evaluation of a Data-driven Scenario Generation Framework for Game-based Training,”vol. 9, no. 3, pp. 213-226, Sept. 2017.**IEEE Transactions on Computational Intelligence and AI in Games**, - Ying Li, Zhixing Huang,
**J. Zhong***, L. Feng, “Genetic Programming for Lifetime Maximization in Wireless Sensor Networks with a Mobile Sink”,**Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science** - Z. Huang, W. Liu and
**J. Zhong***, “Estimating the Real Positions of Objects in Images by Using Evolutionary Algorithm,”, Singapore, 2017, pp. 34-39.**2017 International Conference on Machine Vision and Information Technology (CMVIT)**

**2016**

**J. Zhong**, Y.-S. Ong, and W. Cai, “Self-Learning Gene Expression Programming,”, Vol.20, No. 1, pp.65-80, 2016.*IEEE Transactions on Evolutionary Computation*- Y. Xue,
**J. Zhong***, Tian Huat Tan, Yang Liu, Wentong Cai, Manman Chen, Jun Sun, “IBED: Combining IBEA and DE for Optimal Feature Selection in Software Product Line Engineering,”, Vol.49, pp.1215-1231, 2016.*Applied Soft Computing* **J. Zhong,**Wentong Cai, Linbo Luo, Mingbi Zhao, “Learning behavior patterns from video for agent-based crowd modeling and simulation,”, Vol.30, No.5, pp.990-1019, 2016.*Autonomous Agents and Multi-Agent Systems*- L. Zhou, L Feng,
**J Zhong**, et al. “Evolutionary multitasking in combinatorial search spaces: A case study in capacitated vehicle routing problem,”, IEEE, 2016, pp.1-8.*2016 IEEE Symposium Series on Computational Intelligence (SSCI)* **J. Zhong**and Wentong Cai, “A Hyper-Heuristic Framework for Agent-Based Crowd Modeling and Simulation,”, pp.1331-1332.*2016 International Conference on Autonomous Agents and Multiagent Systems (AAMAS2016)*- M. Zhao,
**J. Zhong**, and Wentong Cai, “A Role-dependent Data-driven Approach for High Density Crowd Behavior Modeling,”*SIGSIM Conference on Principles of Advanced Discrete Simulation**(*2016, pp.89-97.**SIGSIM-PADS),**

**2015 and Before**

**J. Zhong**, and W. Cai, “Differential Evolution with Sensitivity Analysis and the Powell’s Method for Crowd Model Calibration,”, 2015, Vol.9, pp. 26-32, 2015.*Journal of Computational Science***J. Zhong**, N. Hu, W. Cai, M. Lees, and L.B. Luo, “Density-Based Evolutionary Framework for Crowd Model Calibration,”, Vol.6, pp. 11-22, 2015.*Journal of Computational Science*-
**J.Zhong**, W. Cai, L. Luo, and H. Yin, “Learning Behavior Patterns from Video: A Data-driven Framework for Agent-based Crowd Modeling,”pp.801-809, International Foundation for Autonomous Agents and Multiagent Systems, 2015.*In Proceedings of the 2015 International Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2015),* **J. Zhong**, L. Luo, W. Cai, and M. Lees, “Automatic Rule Identification for Agent-Based Crowd Models Through Gene Expression Programming,”pp.1125-1132, International Foundation for Autonomous Agents and Multiagent Systems, 2014.*In Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2014),***J. Zhong**, W. Cai, and L.B. Luo, “Crowd Evacuation Planning Using Cartesian Genetic Programming and Agent-Based Crowd Modeling,”Huntington Beach, CA, 2015, pp. 127-138.*In Proceedings of the 2015 Winter Simulation Conference (WSC 2015),***J. Zhong**, L. Luo, W. Cai, and M. Lees, “EA-Based Evacuation Planning Using Agent-Based Crowd Simulation”,pp.395-406. IEEE Press, 2014.*In Proceedings of the 2014 Winter Simulation Conference (WSC 2014),*-
**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,”, Vol. 17, No. 4, pp.512-527, August 2013.*IEEE Transactions on Evolutionary Computation* **J. Zhong**and J. Zhang, “SDE: A Stochastic Coding Differential Evolution for Global Optimization,”, pp.975-981, 2012.*In Proceedings of the 2012 Genetic and evolutionary computation Conference (GECCO2012)***J. Zhong**and J. Zhang, “Ant Colony Optimization Algorithm for Lifetime Maximization in Wireless Sensor Network with Mobile Sink,”, pp. 1199-1204, July, 2012.*In Proceedings of the 2012 Genetic and evolutionary computation Conference**(GECCO 2012)***J. Zhong**and J. Zhang, “Energy-efficient local wake-up scheduling in wireless sensor networks,”, pp.2280-2284, 2011.*In Proceedings of the 2011 IEEE Congress on Evolutionary Computation (CEC 2011)***J. Zhong**and J. Zhang, “Adaptive Multi-objective Differential Evolution with Stochastic Coding Strategy,”, pp. 665-672. July, 2011.*In Proceedings of the 2011 Genetic and evolutionary computation Conference**(GECCO 2011)*- 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,”, Vol.6, pp.68-75, Nov. 2011.*IEEE Computational Intelligence Magazine* **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.**J. Zhong**, X. M. Hu, J. Zhang and M. Gu, “Comparison of Performance between Different Selection Strategies on Simple Genetic Algorithms,”, pp. 1115-1121, November, 2005.*In Proceedings of the 2005 IEEE Computational Intelligence for Modeling, Control and Automation*