Genetic Programming Bibliography entries for Kang Tai

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GP coauthors/coeditors: Akhil Garg, 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, Vishal Jain, Nikilesh Krishnakumar, Biranchi Narayan Panda, D Y Zhao, Kuldip Singh Sangwan,

Genetic Programming Articles by Kang Tai

  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. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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 Kang Tai

  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