Genetic Programming Bibliography entries for Akhil Garg

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GP coauthors/coeditors: Kang Tai, Sriram Srivatsav, Yogesh Bhalerao, L Rachmawati, M M Savalani, A K Gupta, Ankit Garg, Sekharan Sreedeep, Venkatesh Vijayaraghavan, Siba Sankar Mahapatra, Chee How Wong, Liang Gao, K Sumithra, Pravin M Singru, C H Lee, S Barontini, Alexia Stokes, Jinhui Li, Jinjun Hou, Christian Berretta, Jasmine Siu Lee Lam, Vishal Jain, Nikilesh Krishnakumar, Biranchi Narayan Panda, D Y Zhao, Shrutidhara Sarma, Jian Zhang2, Xiongbin Peng, My Loan Phung Le, Kapil Pareek, C M M Chin, Wei Li, Surinder Singh, Xujian Cui, Z Fan, Harpreet Singh, Li Wei, Ankit Goyal, Mei-Juan Xu, Chee Pin Tan, Shaosen Su, Fan Li, Yuhao Huang, Saeed Asghari, Zhang Yi, P Kalita, Paweena Prapainainar, Dazhi Jiang, Wan-Huan (Hanna) Zhou, Kurugodu Harsha Vardhan, Sanandam Bordoloi, Yi Hong, A H Gandomi, K Shankhwar, Marco Leite, Bibhuti Bhusan Biswal, Xiaodong Niu, Xu Meijuan, Jayne Sandoval, Yongsheng Li, Quan Zhou, Vandana, Bibaswan Bose, R Vijayaraghavan, Kuldip Singh Sangwan, Guoxing Lu, Liu Yun, Dezhi Chen, Chin-Tsan Wang, Sivasriprasanna Maddila, Zhun Fan, P Buragohain, Vikas Pratap Singh,

Genetic Programming Articles by Akhil Garg

  1. 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

  2. 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

  3. 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

  4. Biranchi Panda and K. Shankhwar and Akhil Garg and M. M. Savalani. Evaluation of genetic programming-based models for simulating bead dimensions in wire and arc additive manufacturing. Journal of Intelligent Manufacturing, 30(2) 2019. details

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. Biranchi Panda and Marco Leite and Bibhuti Bhusan Biswal and Xiaodong Niu and Akhil Garg. Experimental and numerical modelling of mechanical properties of 3D printed honeycomb structures. Measurement, 116:495-506, 2018. details

  13. H. V. Kurugodu and Sanandam Bordoloi and Yi Hong and Ankit Garg and Akhil Garg and Sekharan Sreedeep and A. H. Gandomi. Genetic programming for soil-fiber composite assessment. Advances in Engineering Software, 122:50-61, 2018. details

  14. 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

  15. Yuhao Huang and Akhil Garg and Saeed Asghari and Xiongbin Peng and My Loan Phung Le. Robust model for optimization of forming process for metallic bipolar plates of cleaner energy production system. International Journal of Hydrogen Energy, 43(1):341-353, 2018. details

  16. Akhil Garg and Xiongbin Peng and My Loan Phung Le and Kapil Pareek and C. M. M. Chin. Design and analysis of capacity models for Lithium-ion battery. Measurement, 120:114-120, 2018. details

  17. 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

  18. A. Garg and Jasmine Siu Lee Lam and B. N. Panda. A hybrid computational intelligence framework in modelling of coal-oil agglomeration phenomenon. Applied Soft Computing, 55:402-412, 2017. details

  19. A. Garg and Jasmine Siu Lee Lam. Design of explicit models for estimating efficiency characteristics of microbial fuel cells. Energy, 134:136-156, 2017. details

  20. Ankit Garg and Jinhui Li and Jinjun Hou and Christian Berretta and Akhil Garg. A new computational approach for estimation of wilting point for green infrastructure. Measurement, 111:351-358, 2017. details

  21. Wan-Huan Zhou and Ankit Garg and Akhil Garg. Study of the volumetric water content based on density, suction and initial water content. Measurement, 94:531-537, 2016. 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. Harsha Vardhan and Ankit Garg and Jinhui Li and Akhil Garg. Measurement of Stress Dependent Permeability of Unsaturated Clay. Measurement, 91:371-376, 2016. details

  24. Biranchi Narayan Panda and Akhil Garg and K. Shankhwar. Empirical investigation of environmental characteristic of 3-D additive manufacturing process based on slice thickness and part orientation. Measurement, 86:293-300, 2016. details

  25. Dazhi Jiang and Wan-Huan Zhou and Ankit Garg and Akhil Garg. Model development and surface analysis of a bio-chemical process. Chemometrics and Intelligent Laboratory Systems, 157:133-139, 2016. details

  26. 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

  27. 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

  28. Akhil1 Garg and Jasmine Siu Lee Lam. Power consumption and tool life models for the production process. Journal of Cleaner Production, 131:754-764, 2016. details

  29. Akhil Garg and B. N. Panda and D. Y. Zhao and K. Tai. Framework based on number of basis functions complexity measure in investigation of the power characteristics of direct methanol fuel cell. Chemometrics and Intelligent Laboratory Systems, 155:7-18, 2016. details

  30. 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

  31. Akhil Garg and Kang Tai. Evolving genetic programming models of higher generalization ability in modelling of turning process. Engineering Computations, 32(8):2216-2234, 2015. details

  32. 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

  33. Akhil1 Garg and V. Vijayaraghavan and K. Tai and Pravin M. Singru and Vishal Jain and Nikilesh Krishnakumar. Model development based on evolutionary framework for condition monitoring of a lathe machine. Measurement, 73:95-110, 2015. details

  34. Akhil1 Garg and Jasmine Siu Lee Lam. Measurement of environmental aspect of 3-D printing process using soft computing methods. Measurement, 75:210-217, 2015. details

  35. Akhil1 Garg and Jasmine Siu Lee Lam. Improving environmental sustainability by formulation of generalized power consumption models using an ensemble based multi-gene genetic programming approach. Journal of Cleaner Production, 102:246-263, 2015. details

  36. 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

  37. A. Garg and K. Tai and C. H. Lee and M. M. Savalani. A hybrid M5'-genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process. Journal of Intelligent Manufacturing, 25(6):1349-1365, 2014. details

  38. A. Garg and K. Tai and A. K. Gupta. A modified multi-gene genetic programming approach for modelling true stress of dynamic strain aging regime of austenitic stainless steel 304. Meccanica, 49(5) 2014. details

  39. A. Garg and K. Tai and M. M. Savalani. Formulation of bead width model of an SLM prototype using modified multi-gene genetic programming approach. The International Journal of Advanced Manufacturing Technology, 73(1 - 4) 2014. details

  40. Ankit Garg and Akhil Garg and K. Tai and S. Sreedeep. Estimation of Pore Water Pressure of Soil Using Genetic Programming. Geotechnical and Geological Engineering, 32(4) 2014. details

  41. Akhil Garg and Ankit Garg and K. Tai. A multi-gene genetic programming model for estimating stress-dependent soil water retention curves. Computational Geosciences, 18(1) 2014. details

  42. 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

  43. V. Vijayaraghavan and A. Garg and C. H. Wong and K. Tai and S. S. Mahapatra. Measurement of properties of graphene sheets subjected to drilling operation using computer simulation. Measurement, 50:50-62, 2014. details

  44. Ankit Garg and Akhil Garg and K. Tai and S. Barontini and Alexia Stokes. A Computational Intelligence-Based Genetic Programming Approach for the Simulation of Soil Water Retention Curves. Transport in Porous Media, 103(3):497-513, 2014. details

  45. 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

  46. 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

  47. A. Garg and V. Vijayaraghavan and S. S. Mahapatra and K. Tai and C. H. Wong. Performance evaluation of microbial fuel cell by artificial intelligence methods. Expert Systems with Applications, 41(4, Part 1):1389-1399, 2014. details

  48. Akhil Garg and Ankit Garg and K. Tai and S. Sreedeep. Estimation of factor of safety of rooted slope using an evolutionary approach. Ecological Engineering, 64:314-324, 2014. details

  49. Akhil Garg and Ankit Garg and K. Tai and S. Sreedeep. An integrated SRM-multi-gene genetic programming approach for prediction of factor of safety of 3-D soil nailed slopes. Engineering Applications of Artificial Intelligence, 30:30-40, 2014. details

  50. A. Garg and K. Tai. Stepwise approach for the evolution of generalized genetic programming model in prediction of surface finish of the turning process. Advances in Engineering Software, 78:16-27, 2014. details

  51. A. Garg and L. Rachmawati and K. Tai. Classification-driven model selection approach of genetic programming in modelling of turning process. The International Journal of Advanced Manufacturing Technology, 69(5 - 8) 2013. details

  52. Akhil Garg and Yogesh Bhalerao and Kang Tai. Review of empirical modelling techniques for modelling of turning process. International Journal of Modelling, Identification and Control, Vol. 20, No. 2, 2013, 20(2):121-129, 2013. details

Genetic Programming conference papers by Akhil Garg

  1. Vandana and Bibaswan Bose and Akhil Garg. Sensitivity Analysis of Battery Digital Twin Design Variables Using Genetic Programming. In 2023 IEEE 3rd International Conference on Sustainable Energy and Future Electric Transportation (SEFET), 2023. details

  2. Akhil Garg and Jasmine Siu Lee Lam and M. M. Savalani. A New Variant of Genetic Programming in Formulation of Laser Energy Consumption Model of 3D Printing Process. In Handbook of Sustainability in Additive Manufacturing, 2016. Springer. details

  3. Akhil Garg and Kang Tai. An Improved Multi-Gene Genetic Programming Approach for the Evolution of Generalized Model in Modelling of Rapid Prototyping Process. In Moonis Ali and Jeng-Shyang Pan and Shyi-Ming Chen and Mong-Fong Horng editors, Modern Advances in Applied Intelligence - 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014, Kaohsiung, Taiwan, June 3-6, 2014, Proceedings, Part I, volume 8481, pages 218-226, 2014. Springer. details

  4. Akhil Garg and Kang Tai. Genetic Programming for Modeling Vibratory Finishing Process: Role of Experimental Designs and Fitness Functions. In Bijaya Ketan Panigrahi and Ponnuthurai Nagaratnam Suganthan and Swagatam Das and Subhransu Sekhar Dash editors, Proceedings of the 4th International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO 2013), Part II, volume 8298, pages 23-31, Chennai, India, 2013. Springer. details

  5. A. Garg and K. Tai. Selection of a robust experimental design for the effective modeling of nonlinear systems using Genetic Programming. In Barbara Hammer and Zhi-Hua Zhou and Lipo Wang and Nitesh Chawla editors, IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013, pages 287-292, Singapore, 2013. details

  6. A. Garg and S. Sriram and K. Tai. Empirical Analysis of Model Selection Criteria for Genetic Programming in Modeling of Time Series System. In P. N. Suganthan editor, 2013 IEEE Symposium Series on Computational Intelligence, pages 90-94, Singapore, 2013. details

  7. A. Garg and K. Tai. Comparison of regression analysis, Artificial Neural Network and genetic programming in Handling the multicollinearity problem. In Proceedings of International Conference on Modelling, Identification Control (ICMIC 2012), pages 353-358, Wuhan, China, 2012. details

  8. A. Garg and K. Tai. Review of genetic programming in modeling of machining processes. In Proceedings of International Conference on Modelling, Identification Control (ICMIC 2012), pages 653-658, Wuhan, China, 2012. details