Genetic Programming Bibliography entries for Akhil Garg

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GP coauthors/coeditors: Kang Tai, Sriram Srivatsav, Yogesh Bhalerao, Ankit Garg, S Sreedeep, Venkatesh Vijayaraghavan, Siba Sankar Mahapatra, Chee How Wong, Liang Gao, K Sumithra, Pravin M Singru, C H Lee, M M Savalani, S Barontini, Alexia Stokes, Jasmine Siu Lee Lam, Vishal Jain, Nikilesh Krishnakumar, Biranchi Narayan Panda, D Y Zhao, Shrutidhara Sarma, Jian Zhang, Dazhi Jiang, Wan-Huan Zhou, K Shankhwar, Kurugodu Harsha Vardhan, Jinhui Li, R Vijayaraghavan, Kuldip Singh Sangwan, Guoxing Lu,

Genetic Programming Articles by Akhil Garg

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. Akhil Garg and Shrutidhara Sarma and B. N. Panda and Jian Zhang 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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. J. Intelligent Manufacturing, 25(6):1349-1365, 2014. details

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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

  24. 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

  25. 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

  26. 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

  27. 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

  28. 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. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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