Full publications

“*” indicates corresponding author.

2019

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. Z. Huang, J. Zhong*, L. Feng, Y. Mei, and W. Cai, “ A Fast Parallel Genetic Programming Framework with Adaptively Weighted Primitives For Symbolic Regression ,” Soft Computing, 2019, Accepted.
  8. Y. Li, Y. Chen, J. Zhong*, and Z. Huang, “Niching Particle Swarm Optimization with Equilibrium Factor for Multi-modal Optimization,” Information Sciences, 2019, Accepted
  9. Z. Huang, C. Lu, and J. Zhong, “A Multi-Objective Hyper-Heuristic for Unmanned Aerial Vehicle Data Collection in Wireless Sensor Networks”, SSCI 2019, Accepted.
  10. J. Zhong*, L. Li, W. Liu, L. Feng, and X. Hu, “A Co-evolutionary Cartesian Genetic Programming with Adaptive Knowledge Transfer,” CEC2019, Accepted
  11. 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,” CEC2019, Accepted

2018

  1. 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.
  2. L. Feng, L. Zhou, J. Zhong, A. Gupta, Y-S. Ong, K.C. Tan and A. K. Qin, “Evolutionary Multi-tasking via Explicit Autoencoding,” IEEE Transactions on Cybernetics, In Press 2018
  3. 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.
  4. N. Hu, J. Zhong, et al. “Guide them through: an automatic crowd control framework using multi-objective genetic programming,” Applied Soft Computing, Volume 66, Pages 90-103, 2018.
  5. J. Zhong*, Y. Lin, C. Lu, and Z. Huang, “A Deep Learning Assisted Gene Expression Programming Framework for Symbolic Regression Problems”, ICONIP(7) 2018:530-541 .
  6. Y. Chen, J. Zhong*, “A Fast Memetic Multi-objective Differential Evolution for Multi-tasking Optimization”, 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, 2018, pp. 1-8. doi: 10.1109/CEC.2018.8477722 .
  7. D. Liu, S. Huang, J. Zhong*, “Surrogate-assisted Multi-tasking Memetic Algorithm”, 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, 2018, pp. 1-8. doi: 10.1109/CEC.2018.8477830.
  8. S. Huang, J. Zhong*, “ES-GP: An Effective Evolutionary Regression Framework with Gaussian Process and Adaptive Segmentation Strategy”, International Conference on Computational Science (ICCS). Springer, Cham, 2018: 736-743.
  9. Y. Chen, J. Zhong*, M. Tan, “Comprehensive Learning Gene Expression Programming for Automatic Implicit Equation Discovery, ” International Conference on Computational Science (ICCS). Springer, Cham, 2018: 114-128.

2017

  1. J. Zhong, W. Cai, M. Lees, L. Luo, “Automatic Model Construction for the Behaviour of Human Crowds,” Applied Soft Computing, Vol.56, pp.368-378, July, 2017.
  2. J. Zhong, L. Feng, and Yew-Soon, Ong, “Gene Expression Programming: A Survey,” IEEE Computational Intelligence Magazine, 12(3):54-72, 2017
  3. L. Luo, X. Hou, J. Zhong, et al. “Sampling-based adaptive bounding evolutionary algorithm for continuous optimization problems,” Information Sciences, Vol.382, pp.216-233, 2017.
  4. 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 Gamesvol. 9, no. 3, pp. 213-226, Sept. 2017.
  5. 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, vol 10593, pp.774-785, Springer, Cham .
  6. Z. Huang, W. Liu and J. Zhong*, “Estimating the Real Positions of Objects in Images by Using Evolutionary Algorithm,” 2017 International Conference on Machine Vision and Information Technology (CMVIT), Singapore, 2017, pp. 34-39.

2016

  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. 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,”Applied Soft Computing, Vol.49, pp.1215-1231, 2016.
  3. 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.
  4. L. Zhou, L Feng, J Zhong, et al. “Evolutionary multitasking in combinatorial search spaces: A case study in capacitated vehicle routing problem,” 2016 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2016, pp.1-8.
  5. J. Zhong and Wentong Cai, “A Hyper-Heuristic Framework for Agent-Based Crowd Modeling and Simulation,” 2016 International Conference on Autonomous Agents and Multiagent Systems (AAMAS2016), pp.1331-1332.
  6. 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 (SIGSIM-PADS), 2016, pp.89-97.

2015 and Before

  1. 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.
  2. 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.
  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, W. Cai, and L.B. Luo, “Crowd Evacuation Planning Using Cartesian Genetic Programming and Agent-Based Crowd Modeling,” In Proceedings of the 2015 Winter Simulation Conference (WSC 2015), Huntington Beach, CA, 2015, pp. 127-138.
  6. J. Zhong, L. Luo, W. Cai, and M. Lees, “EA-Based Evacuation Planning Using Agent-Based Crowd Simulation”, In Proceedings of the 2014 Winter Simulation Conference (WSC 2014), pp.395-406. IEEE Press, 2014.
  7. 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.
  8. 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.
  9. 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, July, 2012.
  10. 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.
  11. 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. July, 2011.
  12. 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.
  13. 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.
  14. J. Zhong, X. M. Hu, J. Zhang and M. Gu, “Comparison of Performance between Different Selection Strategies on Simple Genetic Algorithms,” In Proceedings of the 2005 IEEE Computational Intelligence for Modeling, Control and Automation, pp. 1115-1121, November, 2005.