Genetic Programming Bibliography entries for Jinghui Zhong
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Joey Tianyi Zhou,
Genetic Programming Articles by Jinghui Zhong
Jinghui Zhong and Liang Feng and Wentong Cai and Yew-Soon Ong.
Multifactorial Genetic Programming for Symbolic Regression Problems.
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(11):4492-4505, 2020.
Jinghui Zhong and Zhixing Huang and Liang Feng and Wan Du and Ying Li.
A hyper-heuristic framework for lifetime maximization in wireless sensor networks with a mobile sink.
IEEE/CAA Journal of Automatica Sinica, 7(1):223-236, 2020.
Dongrui Li and Jinghui Zhong.
Dimensionally Aware Multi-Objective Genetic Programming for Automatic Crowd Behavior Modeling.
ACM Trans. Model. Comput. Simul., 30(3):19:1-19:24, 2020.
Zhixing Huang and Jinghui Zhong and Liang Feng and Yi Mei and Wentong Cai.
A fast parallel genetic programming framework with adaptively weighted primitives for symbolic regression.
Soft Comput., 24(10):7523-7539, 2020.
Tiantian Cheng and Jinghui Zhong.
An efficient memetic genetic programming framework for symbolic regression.
Memetic Comput., 12(4):299-315, 2020.
Nan Hu and Jinghui Zhong and Joey Tianyi Zhou and Suiping Zhou and Wentong Cai and Christopher Monterola.
Guide them through: An automatic crowd control framework using multi-objective genetic programming.
Applied Soft Computing, 66:90-103, 2018.
Jinghui Zhong and Liang Feng and Yew-Soon Ong.
Gene Expression Programming: A Survey [Review Article].
IEEE Computational intelligence magazine, 12(3):54-72, 2017.
Jinghui Zhong and Wentong Cai and Michael Lees and Linbo Luo.
Automatic model construction for the behavior of human crowds.
Applied Soft Computing, 56:368-378, 2017.
Jinghui Zhong and Yew-Soon Ong and Wentong Cai.
Self-Learning Gene Expression Programming.
IEEE Transactions on Evolutionary Computation, 20(1):65-78, 2016.
Qin-zhe Xiao and Jinghui Zhong and Liang Feng and Linbo Luo and Jianming Lv.
A Cooperative Coevolution Hyper-Heuristic Framework for Workflow Scheduling Problem.
IEEE Transactions on Services Computing.
Tingyang Wei and Wei-Li Liu and Jinghui Zhong and Yue-Jiao Gong.
Multiclass Classification on High Dimension and Low Sample Size Data using Genetic Programming.
IEEE Transactions on Emerging Topics in Computing.
Zhixing Huang and Yi Mei and Jinghui Zhong.
Semantic Linear Genetic Programming for Symbolic Regression.
IEEE Transactions on Cybernetics.
Genetic Programming conference papers by Jinghui Zhong
Lianjie Zhong and Jinghui Zhong and Chengyu Lu.
A Comparative Analysis of Dimensionality Reduction Methods for Genetic Programming to Solve High-Dimensional Symbolic Regression Problems. In
2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 476-483, 2021.
Ruihua Zeng and Zhixing Huang and Yongliang Chen and Jinghui Zhong and Liang Feng.
Comparison of Different Computing Platforms for Implementing Parallel Genetic Programming. In
Yaochu Jin editor,
2020 IEEE Congress on Evolutionary Computation, CEC 2020, page paper id24270, internet, 2020. IEEE Press.
Jinghui Zhong and Linhao Li and Wei-Li Liu and Liang Feng and Xiao-Min Hu.
A Co-evolutionary Cartesian Genetic Programming with Adaptive Knowledge Transfer. In
Carlos A. Coello Coello editor,
2019 IEEE Congress on Evolutionary Computation, CEC 2019, pages 2665-2672, Wellington, New Zealand, 2019. IEEE Press.
Zhixing Huang and Chengyu Lu and Jinghui Zhong.
A Multi-Objective Hyper-Heuristic for Unmanned Aerial Vehicle Data Collection in Wireless Sensor Networks. In
2019 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1614-1621, 2019.
Jinghui Zhong and Yusen Lin and Chengyu Lu and Zhixing Huang.
A Deep Learning Assisted Gene Expression Programming Framework for Symbolic Regression Problems. In
Long Cheng and Andrew Chi-Sing Leung and Seiichi Ozawa editors,
Neural Information Processing - 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part VII, volume 11307, pages 530-541, 2018. Springer.
Zhixing Huang and Jinghui Zhong and Weili Liu and Zhou Wu.
Multi-population genetic programming with adaptively weighted building blocks for symbolic regression. In
Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and Shigeru Obayashi and Bogdan Filipic and Thomas Bartz-Beielstein and Grant Dick and Masaharu Munetomo and Silvino Fernandez Alzueta and Thomas Stuetzle and Pablo Valledor Pellicer and Manuel Lopez-Ibanez and Daniel R. Tauritz and Pietro S. Oliveto and Thomas Weise and Borys Wrobel and Ales Zamuda and Anne Auger and Julien Bect and Dimo Brockhoff and Nikolaus Hansen and Rodolphe Le Riche and Victor Picheny and Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and Richard Duro and Joshua Auerbach and Harold de Vladar and Antonio J. Fernandez-Leiva and JJ Merelo and Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and Francisco Chavez de la O and Ozgur Akman and Khulood Alyahya and Juergen Branke and Kevin Doherty and Jonathan Fieldsend and Giuseppe Carlo Marano and Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and Riyad Alshammari and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo and John McCall and Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and Masaya Nakata and Anthony Stein and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and William La Cava and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Ivanoe De Falco and Antonio Della Cioppa and Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and Giovanni Iacca and Ahmed Hallawa and Anil Yaman and Alma Rahat and Handing Wang and Yaochu Jin and David Walker and Richard Everson and Akira Oyama and Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and Pramudita Satria Palar editors,
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 266-267, Kyoto, Japan, 2018. ACM.
Tiantian Cheng and Jinghui Zhong.
An Efficient Cooperative Co-Evolutionary Gene Expression Programming. In
2018 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pages 1422-1427, 2018.
Yongliang Chen and Jinghui Zhong and Mingkui Tan.
Comprehensive Learning Gene Expression Programming for Automatic Implicit Equation Discovery. In
Yong Shi and Haohuan Fu and Yingjie Tian and Valeria V. Krzhizhanovskaya and Michael Harold Lees and Jack J. Dongarra and Peter M. A. Sloot editors,
Computational Science - ICCS 2018 - 18th International Conference, Wuxi, China, June 11-13, 2018, Proceedings, Part I, volume 10860, pages 114-128, 2018. Springer.
Qin-zhe Xiao and Jinghui Zhong and Wen-Neng Chen and Zhi-Hui Zhan and Jun Zhang.
Indicator-based Multi-objective Genetic Programming for Workflow Scheduling Problem. In
Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 217-218, Berlin, Germany, 2017. ACM.
Ying Li and Zhixing Huang and Jinghui Zhong and Liang Feng.
Genetic Programming for Lifetime Maximization in Wireless Sensor Networks with a Mobile Sink. In
Yuhui Shi and Kay Chen Tan and Mengjie Zhang and Ke Tang and Xiaodong Li and Qingfu Zhang and Ying Tan and Martin Middendorf and Yaochu Jin editors,
Proceedings of the 11th International Conference on Simulated Evolution and Learning, SEAL-2017, volume 10593, pages 774-785, Shenzhen, China, 2017. Springer.
Jinghui Zhong and Wentong Cai and Linbo Luo.
Crowd evacuation planning using Cartesian Genetic Programming and agent-based crowd modeling. In
2015 Winter Simulation Conference (WSC), pages 127-138, 2015.
Jinghui Zhong and Linbo Luo and Wentong Cai and Michael Lees.
Automatic Rule Identification for Agent-Based Crowd Models Through Gene Expression Programming. In
Alessio Lomuscio and Paul Scerri and Ana Bazzan and Michael Huhns editors,
13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014), page 1125), Paris, 2014. ACM.