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,
  author =       "A. Garg and K. Tai",
  title =        "Stepwise approach for the evolution of generalized
                 genetic programming model in prediction of surface
                 finish of the turning process",
  journal =      "Advances in Engineering Software",
  volume =       "78",
  pages =        "16--27",
  year =         "2014",
  ISSN =         "0965-9978",
  DOI =          "doi:10.1016/j.advengsoft.2014.08.005",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0965997814001318",
  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.",
  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