Full publications

 “*” indicates corresponding author.

2023

  1. 钟竞辉，林育钿，李稳强，蔡文桐，基于数字孪生的机场人群智慧管控技术，系统仿真学报，2023-02-17，https://kns.cnki.net/kcms/detail//11.3092.V.20230216.1701.005.html
  2. 麦伟杰，刘伟莉，钟竞辉*，基于机器学习的演化多任务优化框架，计算机学报，2023，已接收
  3. J. Zhong, T. Cheng, W. -L. Liu, P. Yang, Y. Lin and J. Zhang, “An Evolutionary Guardrail Layout Design Framework for Crowd Control in Subway Stations,” IEEE Transactions on Computational Social Systems, vol. 10, no. 1, pp. 297-310, Feb. 2023, doi: 10.1109/TCSS.2022.3140310.
  4. X.-C. Liao, W.-N. Chen, X.-Q. Xiao, J. Zhong, X.-M. Hu, “Crowd Management through Optimal Layout of Fences: An Ant Colony Approach Based on Crowd Simulation,” IEEE Transactions on Intelligent Transportation Systems, 2023, Accepted.  
  5. Y. Li and J. Zhong*,“HAS-EA: a fast parallel surrogate-assisted evolutionary algorithm,” Memetic Computing,15, 103–115 (2023). https://doi.org/10.1007/s12293-022-00376-7
  6. Z. Tan, L. Luo, J. Zhong, “Knowledge transfer in evolutionary multi-task optimization: A survey,” Applied Soft Computing, Volume 138, 2023, https://doi.org/10.1016/j.asoc.2023.110182.
  7. C. Jiang, Y. Gao, W.-W. Ng, J. Zhou, J. Zhong, H. Zhen， “SeDepTTS: Enhancing the Naturalness via Semantic Dependency and Local Convolution for Text-to-Speech Synthesis，”  AAAI, 2023, Accepted. 
  8. H. Ling, J. Zhong*, J. Dong, S. Wang, S. Wang,Q. Zhang, ” An Adaptive Multiform Evolutionary Algorithm for Global Continuous Optimization”, SSCI, 2023, Accepted.
  9.  R. Zhou, and J. Zhong*, “A Comprehensive Pragmatic Investigation of Batched Acquisition Functions in Bayesian Optimization,” GECCO, 2023, Accepted.
  10. N. GLIGOROVSKI, J. Zhong*,   “LGP-VEC: A Vectorial Linear Genetic Programming for Symbolic Regression,”, GECCO 2023, Accepted.
  11. Z. Yu, W. Liu*, J. Zhong*, T. Huang, X. Lu, Z. Lin, “An Efficient Multitasking Ant Colony Optimization Framework,” IEEE CEC 2023, Accepted.
  12. Q. Ma, J. Zhong*, Y. Yang, W. Liu, Y. Gao, and W. -W.Ng, “A Lightweight and Efficient Model for Audio Anti-Spoofing”, ACM MMAsia2023, Accepted.

2022

  1. T. Wei, S. Wang, J.Zhong*, D. Liu, and J. Zhang, “A Review on Evolutionary Multi-Task Optimization: Trends and Challenges,” IEEE Transactions on Evolutionary Computation, vol. 26, no. 5, pp. 941-960, Oct. 2022, doi: 10.1109/TEVC.2021.3139437.
  2. J. Zhong, D. Li, W. Cai, W-N. Chen, Y. Shi, “Automatic Crowd Navigation Path Planning in Public Scenes through Multi-objective Differential Evolution”,IEEE Transactions on Computational Social Systems,2022,Accepted. DOI: 10.1109/TCSS.2022.3217417
  3. Z. Huang, Y. Mei, and J. Zhong*, “Semantic Linear Genetic Programming for Symbolic Regression,” IEEE Transactions on Cybernetics, 2022, Accepted. DOI:10.1109/TCYB.2022.3181461
  4. J. Dong, J. Zhong*, W.-N. Chen, J. Zhang, , “An Efficient Federated Genetic Programming Framework for Symbolic Regression,” IEEE Transactions on Emerging Topics in Computational Intelligence, 2022, Accepted. DOI:10.1109/TETCI.2022.3201299
  5. J. Zhong, Dongrui Li, Zhixing Huang, Chengyu Lu, and Wentong Cai. “Data-driven Crowd Modeling Techniques: A Survey,” ACM Trans. Model. Comput. Simul. 32, 1, Article 4 (January 2022), 33 pages. 
  6. Q-z. Xiao, J. Zhong*, L. Feng, L. Luo and J. Lv, “A Cooperative Coevolution Hyper-Heuristic Framework for Workflow Scheduling Problem,” in IEEE Transactions on Services Computing, vol. 15, no. 1, pp. 150-163, 1 Jan.-Feb. 2022, doi: 10.1109/TSC.2019.2923912.
  7. J. Zhong, J. Yang, Y. Chen, W. -L. Liu and L. Feng, “Mining Implicit Equations From Data Using Gene Expression Programming,” in IEEE Transactions on Emerging Topics in Computing, vol. 10, no. 2, pp. 1058-1074, 1 April-June 2022, doi: 10.1109/TETC.2021.3068651.
  8. T. Wei, W. -L. Liu, J. Zhong* and Y. -J. Gong, “Multiclass Classification on High Dimension and Low Sample Size Data Using Genetic Programming,” in IEEE Transactions on Emerging Topics in Computing, vol. 10, no. 2, pp. 704-718, 1 April-June 2022, doi: 10.1109/TETC.2020.3034495.
  9. L. Luo, B. Zhang, B. Guo, J. Zhong and W. Cai, “Why They Escape: Mining Prioritized Fuzzy Decision Rule in Crowd Evacuation,” in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 19456-19470, Oct. 2022, doi: 10.1109/TITS.2022.3156060.  
  10. Y.-L. Lan, F. Liu, Z. Huang, W. Ng, and J. Zhong, “Two-Echelon Dispatching Problem with Mobile Satellites in City Logistics,”  IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 1, pp. 84-96, Jan. 2022, doi: 10.1109/TITS.2020.3003598.
  11. Z. Ma, J. Zhong* , W. Liu, and W. Yu, “An  Evolutionary  Framework  for  the Automatic Deployment of Security Guards in Large Public Spaces”, Applied Intelligence,  2022，https://doi.org/10.1007/s10489-022-03975-6 . 
  12. L. Chen, W.-L. Liu*, J. Zhong*, “An Efficient Multi-objective Ant Colony Optimization for Task Allocation of Heterogeneous Unmanned Aerial Vehicles,” Journal of computational Science, Volume 58, 2022. https://doi.org/10.1016/j.jocs.2021.101545.
  13. B. Zhang, J. Zhong, W. Cai, “A Data-Driven Approach for Pedestrian Intention Prediction in Large Public Places,”  In Proceedings of the 2022 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS ’22). Association for Computing Machinery, New York, NY, USA, 33–36. https://doi.org/10.1145/3518997.3531022
  14. W. Liu, Y. Yang and J. Zhong*, “Towards Dual-Modal Crowd Density Forecasting in Transportation Building,” 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 2022, pp. 1-8, doi: 10.1109/IJCNN55064.2022.9892179.
  15. J. Li and J. Zhong*, “Building an Efficient Retrieval-based Dialogue System with Contrastive Learning,” 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 2022, pp. 1-8, doi: 10.1109/IJCNN55064.2022.9892217.

2021

  1. T. Wei, and J. Zhong*, “Towards Generalized Resource Allocation on Evolutionary Multitasking for Multi-Objective Optimization,” IEEE Computational Intelligence Magazine, vol. 16, no. 4, pp. 20-37, Nov. 2021, doi: 10.1109/MCI.2021.3108310.[link] [Code] [Research Frontier paper]
  2. 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, vol. 9, no. 4, pp. 1930-1944, 1 Oct.-Dec. 2021, doi: 10.1109/TETC.2019.2945775.
  3. W.-L. Liu, J. Yang, J. Zhong*, S. Wang, “Genetic programming with separability detection for symbolic regression,” Complex Intell. Syst.7, 1185–1194 (2021).
  4. 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, vol. 5, no. 4, pp. 700-713, Aug. 2021, doi: 10.1109/TETCI.2019.2940978.
  5. L. Zhou, L. Feng, K.C. Tan, J. Zhong, Z. Zhu, K. Liu, C. Chen, “Toward Adaptive Knowledge Transfer in Multifactorial Evolutionary Computation,” IEEE Transactions on Cybernetics, vol.51, no.5, pp. 2563-2576, May 2021.
  6. L. Feng, Y. Huang, L. Zhou,J. Zhong, A. Gupta, K. Tang, and KC. Tan, “Explicit Evolutionary Multi-tasking for Combinatorial Optimization: A Case Study on Capacitated Vehicle Routing Problem,” IEEE Transactions on Cybernetics, vol.51, no.6, pp.3143-3156, June 2021.
  7. J.-Y. 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, vol. 51, no. 4, pp. 2054-2067, April 2021, doi: 10.1109/TSMC.2019.2953336.
  8. L. Feng, L, Zhou, A. Gupta, J. Zhong, Z. Zhu, KC. Tan, K. Qin, “Solving Generalized Vehicle Routing Problem With Occasional Drivers via Evolutionary Multitasking ,” IEEE Transactions on Cybernetics, vol. 51, no. 6, pp. 3171-3184, June 2021, doi: 10.1109/TCYB.2019.2955599.
  9. Z. Miao, J. Zhong*, P. Yang, S. Wang, D. Liu, “Implicit Neural Network for Implicit Data Regression Problems,” In: Mantoro T., Lee M., Ayu M.A., Wong K.W., Hidayanto A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1516. Springer, Cham. https://doi.org/10.1007/978-3-030-92307-5_22
  10. L. Zhong, J. Zhong*, and C. Lu, “A Comparative Analysis of Dimensionality Reduction Methods for Genetic Programming to Solve High-Dimensional Symbolic Regression Problems,” the 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021, Accepted.

2020

  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, vol. 50, no. 11, pp. 4492-4505, Nov. 2020, doi: 10.1109/TSMC.2018.2853719.
  2. 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, vol. 69, no. 3, pp. 1004-1020, Sept. 2020
  3. 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, vol. 4, no. 3, pp. 369-384, June 2020, doi: 10.1109/TETCI.2019.2916051. [Link] [Code] [2023 IEEE TETCI Outstanding Paper]
  4. 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, vol. 7, no. 1, pp. 223-236, January 2020, doi: 10.1109/JAS.2019.1911846.
  5. T. Cheng and J. Zhong*, “An Efficient Memetic Genetic Programming Framework for Symbolic Regression”, Memetic Computing, 12, 299–315 (2020). https://doi.org/10.1007/s12293-020-00311-8
  6. 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, 24, 7523–7539 (2020). https://doi.org/10.1007/s00500-019-04379-4
  7. D. Li and J. Zhong*. 2020. Dimensionally Aware Multi-Objective Genetic Programming for Automatic Crowd Behavior Modeling. ACM Trans. Model. Comput. Simul. 30, 3, Article 19 (July 2020), 24 pages. DOI:https://doi.org/10.1145/3391407
  8. T. Cheng, J. Zhong*, and W. Cai, “Automatical Guardrail Design of Subway Stations through Multi-objective Evolutionary Algorithm”, 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020, pp. 2438-2445.
  9. R. Zeng, Z. Huang, Y. Chen, J. Zhong* and L. Feng，“Comparison of Different Computing Platforms for Implementing Parallel Genetic Programming,” 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1-8.

2019

  1. 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, vol. 49, no. 9, pp. 3457-3470, Sept. 2019, doi: 10.1109/TCYB.2018.2845361.
  2. Y. Li, Y. Chen, J. Zhong*, and Z. Huang, “Niching Particle Swarm Optimization with Equilibrium Factor for Multi-modal Optimization,” Information Sciences, Volume 494, 2019, Pages 233-246, https://doi.org/10.1016/j.ins.2019.01.084.
  3. T. Wei, and J. Zhong*, “A Preliminary Study of Knowledge Transfer in Multi-Classification using Gene Expression Programming,” Frontiers in Neuroscience, 2020 Jan 17;13:1396. doi: 10.3389/fnins.2019.01396.
  4. Z. Huang, C. Lu, and J. Zhong, “A Multi-Objective Hyper-Heuristic for Unmanned Aerial Vehicle Data Collection in Wireless Sensor Networks”, 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019, pp. 1614-1621.
  5. J. Zhong*, L. Li, W. Liu, L. Feng, and X. Hu, “A Co-evolutionary Cartesian Genetic Programming with Adaptive Knowledge Transfer,” 2019 IEEE Congress on Evolutionary Computation (CEC), 2019, pp. 2665-2672.
  6. 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,” 2019 IEEE Congress on Evolutionary Computation (CEC), 2019, pp. 2153-2159, Accepted

2018

  1. 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.
  2. 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.
  3. 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 .
  4. 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 .
  5. 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.
  6. Z. 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.
  7. 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 Games, vol. 9, no. 3, pp. 213-226, Sept. 2017.
  5. Y. Li, Z. 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. [Link] [Code]
  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. Zhongand 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. Zhongand 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. Zhongand 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. Zhongand 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. Zhongand 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. J. Zhang, Z. H. Zhan, Y. Lin, N. Chen, Y. J. Gong,  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.。。mai
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