Investigation of simplification threshold and noise level of input data in numerical simplification of genetic programs

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

@InProceedings{Kinzett:2010:cec,
  author =       "David Kinzett and Mengjie Zhang and Mark Johnston",
  title =        "Investigation of simplification threshold and noise
                 level of input data in numerical simplification of
                 genetic programs",
  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 =     "In tree based Genetic Programming (GP) there is a
                 tendency for program sizes to increase as the run
                 proceeds without a corresponding improvement in
                 fitness. This increases resource usage, both memory and
                 CPU time, and may result in over-fitting the training
                 data. Numerical simplification is a method for removing
                 redundant code from the program trees as the run
                 proceeds. Compared with the canonical genetic
                 programming method, numerical simplification can
                 generate much smaller programs, use much shorter
                 evolutionary training times and achieve comparable
                 effectiveness performance. A key parameter of this
                 method is the simplification threshold. This paper
                 examines whether there exists any relationship between
                 the noise level in the input data and the optimum value
                 for the simplification threshold and, if it exists,
                 what that relationship is. Our results suggest that
                 there is a relationship between the optimum value of
                 the simplification threshold and the level of noise in
                 the input data and that a lower bound for the optimum
                 simplification threshold is equal to the noise level
                 and an upper bound is five times the noise level.",
  DOI =          "doi:10.1109/CEC.2010.5586181",
  notes =        "WCCI 2010. Also known as \cite{5586181}",
}

Genetic Programming entries for David Kinzett Mengjie Zhang Mark Johnston

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