Genetic Programming Bibliography entries for Liang Gao

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GP coauthors/coeditors: Haojie Chen, Xinyu Li, Akhil Garg, Venkatesh Vijayaraghavan, Chee How Wong, Kang Tai, K Sumithra, Pravin M Singru, Jasmine Siu Lee Lam, Shrutidhara Sarma, Biranchi Narayan Panda, Jian Zhang2, Wei Li, Surinder Singh, Xiongbin Peng, Xujian Cui, Z Fan, Harpreet Singh, C M M Chin, Li Wei, Ankit Goyal, Mei-Juan Xu, Chee Pin Tan, Shaosen Su, Fan Li, Prashant Baredar, Yuhao Huang, Zhang Yi, P Kalita, Paweena Prapainainar, Xiaowei Zhou, Haihong Huang, Xinyu Shao, Yuxin Li, Qingzheng Wang, Ling Fu, Yanbin Yu, Wei Zhou, Qihao Liu, Zhibing Lu, Li Nie, Peigen Li, Liping Zhang, Xiaodong Niu, Xu Meijuan, Jayne Sandoval, Guo Di, Vandana, Yongsheng Li, Quan Zhou, R Vijayaraghavan, Kuldip Singh Sangwan, Guoxing Lu, Long Wen, Guohui Zhang, Yang Yang, Liu Yun, Dezhi Chen, Chin-Tsan Wang, Sivasriprasanna Maddila, Zhun Fan, P Buragohain, Vikas Pratap Singh,

Genetic Programming Articles by Liang Gao

  1. Yuxin Li and Xinyu Li and Liang Gao and Zhibing Lu. Multi-agent deep reinforcement learning for dynamic reconfigurable shop scheduling considering batch processing and worker cooperation. Robotics and Computer-Integrated Manufacturing, 91:102834, 2025. details

  2. Yuxin Li and Qihao Liu and Xinyu Li and Liang Gao. Manufacturing resource-based self-organizing scheduling using multi-agent system and deep reinforcement learning. Journal of Manufacturing Systems, 79:179-198, 2025. details

  3. Yuxin Li and Xinyu Li and Liang Gao. Real-time scheduling for production-logistics collaborative environment using multi-agent deep reinforcement learning. Advanced Engineering Informatics, 65:103216, 2025. details

  4. Yuxin Li and Qingzheng Wang and Xinyu Li and Liang Gao and Ling Fu and Yanbin Yu and Wei Zhou. Real-Time Scheduling for Flexible Job Shop With AGVs Using Multiagent Reinforcement Learning and Efficient Action Decoding. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 55(3):2120-2132, 2025. details

  5. Su Shaosen and Guo Di and Vandana and Liang Gao and Wei Li and Akhil Garg. Enhancing battery health estimation using model selection criteria-based genetic programming. Journal of Energy Storage, 102:114077, 2024. details

  6. Wei Li and Xiaowei Zhou and Haihong Huang and Akhil Garg and Liang Gao. Risk-Based Design Optimization via Scenario Generation and Genetic Programming Under Hybrid Uncertainties. J. Comput. Inf. Sci. Eng., 24(10) 2024. details

  7. Haojie Chen and Xinyu Li and Liang Gao. A surrogate-assisted dual-tree genetic programming framework for dynamic resource constrained multi-project scheduling problem. Int. J. Prod. Res., 62(16):5631-5653, 2024. details

  8. Haojie Chen and Xinyu Li and Liang Gao. A guided genetic programming with attribute node activation encoding for resource constrained project scheduling problem. Swarm and Evolutionary Computation, 83:101418, 2023. details

  9. Shaosen Su and Wei Li and Yongsheng Li and Akhil Garg and Liang Gao and Quan Zhou. Multi-objective design optimization of battery thermal management system for electric vehicles. Applied Thermal Engineering, 196:117235, 2021. details

  10. Akhil Garg and Su Shaosen and Liang Gao and Xiongbin Peng and Prashant Baredar. Aging model development based on multidisciplinary parameters for lithium-ion batteries. International Journal of Energy Research, 44(4):2801-2818, 2020. details

  11. Akhil Garg and Shaosen Su and Fan Li and Liang Gao. Framework of model selection criteria approximated genetic programming for optimization function for renewable energy systems. Swarm and Evolutionary Computation, 59:100750, 2020. details

  12. Akhil Garg and Surinder Singh and Liang Gao and Mei-Juan Xu and Chee Pin Tan. Multi-objective optimisation framework of genetic programming for investigation of bullwhip effect and net stock amplification for three-stage supply chain systems. Int. J. Bio Inspired Comput., 16(4):241-251, 2020. details

  13. Liu Yun and Ankit Goyal and Vikas Pratap Singh and Liang Gao and Xiongbin Peng and Xiaodong Niu and Chin-Tsan Wang and Akhil Garg. Experimental coupled predictive modelling based recycling of waste printed circuit boards for maximum extraction of copper. Journal of Cleaner Production, 218:763-771, 2019. details

  14. Liu Yun and Wei Li and Akhil Garg and Sivasriprasanna Maddila and Liang Gao and Zhun Fan and P. Buragohain and Chin-Tsan Wang. Maximization of extraction of Cadmium and Zinc during recycling of spent battery mix: An application of combined genetic programming and simulated annealing approach. Journal of Cleaner Production, 218:130-140, 2019. details

  15. Shrutidhara Sarma and Ankit Goyal and Liang Gao and Xiaodong Niu and Akhil Garg and Xu Meijuan and Jayne Sandoval. Thermal performance of thin film heat gauges of gold, silver and nano-composite. Applied Thermal Engineering, 147:545-550, 2019. details

  16. Akhil Garg and Li Wei and Ankit Goyal and Xujian Cui and Liang Gao. Evaluation of batteries residual energy for battery pack recycling: Proposition of stack stress-coupled-AI approach. Journal of Energy Storage, 26:101001, 2019. details

  17. Akhil Garg and Liang Gao and Wei Li and Surinder Singh and Xiongbin Peng and Xujian Cui and Z. Fan and Harpreet Singh and C. M. M. Chin. Evolutionary framework design in formulation of decision support models for production emissions and net profit of firm: Implications on environmental concerns of supply chains. Journal of Cleaner Production, 231:1136-1148, 2019. details

  18. Liu Yun and Biranchi Panda and Liang Gao and Akhil Garg and Xu Meijuan and Dezhi Chen and Chin-Tsan Wang. Experimental Combined Numerical Approach for Evaluation of Battery Capacity Based on the Initial Applied Stress, the Real-Time Stress, Charging Open Circuit Voltage, and Discharging Open Circuit Voltage. Mathematical Problems in Engineering, 2018(1):Article ID 8165164, 2018. details

  19. V. Vijayaraghavan and Akhil Garg and Liang Gao. Fracture mechanics modelling of lithium-ion batteries under pinch torsion test. Measurement, 114:382-389, 2018. details

  20. Yuhao Huang and Liang Gao and Zhang Yi and Kang Tai and P. Kalita and Paweena Prapainainar and Akhil Garg. An application of evolutionary system identification algorithm in modelling of energy production system. Measurement, 114:122-131, 2018. details

  21. V. Vijayaraghavan and A. Garg and K. Tai and Liang Gao. Thermo-mechanical modeling of metallic alloys for nuclear engineering applications. Measurement, 97:242-250, 2017. details

  22. V. Vijayaraghavan and A. Garg and Liang Gao and R. Vijayaraghavan and Guoxing Lu. A finite element based data analytics approach for modeling turning process of Inconel 718 alloys. Journal of Cleaner Production, 137:1619-1627, 2016. details

  23. Akhil Garg and Shrutidhara Sarma and B. N. Panda and Jian Zhang2 and L. Gao. Study of effect of nanofluid concentration on response characteristics of machining process for cleaner production. Journal of Cleaner Production, 135:476-489, 2016. details

  24. Akhil1 Garg and Jasmine Siu Lee Lam and L. Gao. Modeling multiple-response environmental and manufacturing characteristics of EDM process. Journal of Cleaner Production, 137:1588-1601, 2016. details

  25. R. Vijayaraghavan and A. Garg and V. Vijayaraghavan and Liang Gao. Development of energy consumption model of abrasive machining process by a combined evolutionary computing approach. Measurement, 75:171-179, 2015. details

  26. Akhil1 Garg and V. Vijayaraghavan and Jasmine Siu Lee Lam and Pravin M Singru and Liang Gao. A molecular simulation based computational intelligence study of a nano-machining process with implications on its environmental performance. Swarm and Evolutionary Computation, 21:54-63, 2015. details

  27. Akhil1 Garg and Jasmine Siu Lee Lam and L. Gao. Energy conservation in manufacturing operations: modelling the milling process by a new complexity-based evolutionary approach. Journal of Cleaner Production, 108, Part A:34-45, 2015. details

  28. V. Vijayaraghavan and A. Garg and C. H. Wong and K. Tai and Pravin M. Singru and Liang Gao and K. S. Sangwan. A molecular dynamics based artificial intelligence approach for characterizing thermal transport in nanoscale material. Thermochimica Acta, 594:39-49, 2014. details

  29. A. Garg and V. Vijayaraghavan and C. H. Wong and K. Tai and K. Sumithra and L. Gao and Pravin M. Singru. Combined CI-MD approach in formulation of engineering moduli of single layer graphene sheet. Simulation Modelling Practice and Theory, 48:93-111, 2014. details

  30. A. Garg and V. Vijayaraghavan and C. H. Wong and K. Tai and Liang Gao. An embedded simulation approach for modeling the thermal conductivity of 2D nanoscale material. Simulation Modelling Practice and Theory, 44:1-13, 2014. details

  31. Yang Yang and Xinyu Li and Liang Gao and Xinyu Shao. A new approach for predicting and collaborative evaluating the cutting force in face milling based on gene expression programming. Journal of Network and Computer Applications, 36(6):1540-1550, 2013. details

  32. X. Y. Li and X. Y. Shao and L. Gao. Optimization of flexible process planning by genetic programming. The International Journal of Advanced Manufacturing Technology, 38(1-2):143-153, 2008. details

Genetic Programming conference papers by Liang Gao