Polynomial modeling for manufacturing processes using a backward elimination based genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Chan:2010:cec2,
  author =       "Kit Yan Chan and Tharam Singh Dillon and 
                 Che Kit Kwong",
  title =        "Polynomial modeling for manufacturing processes using
                 a backward elimination based genetic programming",
  booktitle =    "IEEE Congress on Evolutionary Computation (CEC 2010)",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-6910-9",
  abstract =     "Even if genetic programming (GP) has rich literature
                 in development of polynomial models for manufacturing
                 processes, the polynomial models may contain redundant
                 terms which may cause the overfitted models. In other
                 words, those models have good accuracy on training data
                 sets but poor accuracy on untrained data sets. In this
                 paper, a mechanism which aims at avoiding overfitting
                 is proposed based on a statistical method, backward
                 elimination, which intends to eliminate insignificant
                 terms in polynomial models. By modeling a solder paste
                 dispenser for electronic manufacturing, results show
                 that the insignificant terms in the polynomial model
                 can be eliminated by the proposed mechanism. Results
                 also show that the polynomial model generated by the
                 proposed GP can achieve better predictions than the
                 existing methods.",
  DOI =          "doi:10.1109/CEC.2010.5586309",
  notes =        "WCCI 2010. Also known as \cite{5586309}",
}

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

Citations