Reducing overfitting in manufacturing process modeling using a backward elimination based genetic programming

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@Article{Chan20111648,
  author =       "K. Y. Chan and C. K. Kwong and T. S. Dillon and 
                 Y. C. Tsim",
  title =        "Reducing overfitting in manufacturing process modeling
                 using a backward elimination based genetic
                 programming",
  journal =      "Applied Soft Computing",
  volume =       "11",
  number =       "2",
  pages =        "1648--1656",
  year =         "2011",
  note =         "The Impact of Soft Computing for the Progress of
                 Artificial Intelligence",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2010.04.022",
  URL =          "http://www.sciencedirect.com/science/article/B6W86-501FPF7-6/2/4bf5179fccc0bf3772b121aef439e062",
  keywords =     "genetic algorithms, genetic programming, Process
                 modelling, Polynomial modelling, Overfitting",
  abstract =     "Genetic programming (GP) has demonstrated as an
                 effective approach in polynomial modelling of
                 manufacturing processes. However, polynomial models
                 with redundant terms generated by GP may depict over
                 fitting, while the developed models have good accuracy
                 on trained data sets but relatively poor accuracy on
                 testing data sets. In the literature, approaches of
                 avoiding overfitting in GP are handled by limiting the
                 number of terms in polynomial models. However, those
                 approaches cannot guarantee terms in polynomial models
                 produced by GP are statistically significant to
                 manufacturing processes. In this paper, a statistical
                 method, backward elimination (BE), is proposed to
                 incorporate with GP, in order to eliminate
                 insignificant terms in polynomial models. The
                 performance of the proposed GP has been evaluated by
                 modeling three real-world manufacturing processes,
                 epoxy dispenser for electronic packaging, solder paste
                 dispenser for electronic manufacturing, and punch press
                 system for leadframe downset in IC packaging. Empirical
                 results show that insignificant terms in the polynomial
                 models can be eliminated by the proposed GP and also
                 the polynomial models generated by the proposed GP can
                 achieve results with better predictions than the other
                 commonly used existent methods, which are commonly used
                 in GP for avoiding overfitting in polynomial
                 modeling.",
}

Genetic Programming entries for Kit Yan Chan Che Kit Kwong Tharam S Dillon Y C Tsim

Citations