Genetic Programming Bibliography entries for Jinghui Zhong

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GP coauthors/coeditors: Yongliang Chen, Mingkui Tan, Tiantian Cheng, Nan Hu, Joey Tianyi Zhou, Suiping Zhou, Wentong Cai, Christopher Monterola, Zhixing Huang, Weili Liu, Zhou Wu, Ying Li, Liang Feng, Qin-zhe Xiao, Wen-Neng Chen, Zhi-Hui Zhan, Jun Zhang, Linbo Luo, Michael Lees, Yew-Soon Ong, Yusen Lin, Chengyu Lu,

Genetic Programming Articles by Jinghui Zhong

  1. 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. details

  2. 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. details

  3. 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. details

  4. Jinghui Zhong and Yew-Soon Ong and Wentong Cai. Self-Learning Gene Expression Programming. IEEE Transactions on Evolutionary Computation, 20(1):65-78, 2016. details

  5. 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. details

Genetic Programming conference papers by Jinghui Zhong

  1. 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. details

  2. 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. details

  3. 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. details

  4. 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. details

  5. 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. details

  6. 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. details

  7. 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. details

  8. 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. details