Stepwise approach for the evolution of generalized genetic programming model in prediction of surface finish of the turning process
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- @Article{Garg:2014:AES,
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author = "A. Garg and K. Tai",
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title = "Stepwise approach for the evolution of generalized
genetic programming model in prediction of surface
finish of the turning process",
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journal = "Advances in Engineering Software",
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volume = "78",
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pages = "16--27",
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year = "2014",
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ISSN = "0965-9978",
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DOI = "doi:10.1016/j.advengsoft.2014.08.005",
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URL = "http://www.sciencedirect.com/science/article/pii/S0965997814001318",
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abstract = "Due to the complexity and uncertainty in the process,
the soft computing methods such as regression analysis,
neural networks (ANN), support vector regression (SVR),
fuzzy logic and multi-gene genetic programming (MGGP)
are preferred over physics-based models for predicting
the process performance. The model participating in the
evolutionary stage of the MGGP method is a linear
weighted sum of several genes (model trees) regressed
using the least squares method. In this combination
mechanism, the occurrence of gene of lower performance
in the MGGP model can degrade its performance.
Therefore, this paper proposes a modified-MGGP (M-MGGP)
method using a stepwise regression approach such that
the genes of lower performance are eliminated and only
the high performing genes are combined. In this work,
the M-MGGP method is applied in modelling the surface
roughness in the turning of hardened AISI H11 steel.
The results show that the M-MGGP model produces better
performance than those of MGGP, SVR and ANN. In
addition, when compared to that of MGGP method, the
models formed from the M-MGGP method are of smaller
size. Further, the parametric and sensitivity analysis
conducted validates the robustness of our proposed
model and is proved to capture the dynamics of the
turning phenomenon of AISI H11 steel by unveiling
dominant input process parameters and the hidden
non-linear relationships.",
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keywords = "genetic algorithms, genetic programming, Surface
roughness prediction, Surface property, Turning,
Stepwise regression, Support vector regression",
- }
Genetic Programming entries for
Akhil Garg
Kang Tai
Citations