Using a Tree Structured Genetic Algorithm to Perform Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.3223

  author =       "Ben McKay and Mark J. Willis and Geoffrey W. Barton",
  title =        "Using a Tree Structured Genetic Algorithm to Perform
                 Symbolic Regression",
  booktitle =    "First International Conference on Genetic Algorithms
                 in Engineering Systems: Innovations and Applications,
  year =         "1995",
  editor =       "A. M. S. Zalzala",
  volume =       "414",
  pages =        "487--492",
  address =      "Sheffield, UK",
  publisher_address = "London, UK",
  month =        "12-14 " # sep,
  publisher =    "IEE",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-85296-650-4",
  URL =          "",
  doi =          "doi:10.1049/cp:19951096",
  size =         "6 pages",
  abstract =     "Three examples demonstrate the applicability of the
                 technique within the domain of process engineering",
  notes =        "12--14 September 1995, Halifax Hall, University of
                 Sheffield, UK see also

                 Uses correletion coefficient in fitness function as
                 advocated by M. C. South Phd 1994 {"}The application of
                 GAs to rule finding in data analysis{"}, Newcastle upon
                 Tyne, UK

                 Final fixup? {"}Mutation...replaces a node in the tree
                 with another of the same degree{"}. Elistist. Popsize
                 20, G=100, Pcross=0.8 Pmut=0.5 {"}found to give good
                 performance to date{"}

                 {"}non linear least-squares optimization to obtain
                 'best' value of the (new) constant(s) in the
                 expression{"}. {"}the fitness of a tree is wieghted
                 according to its size{"} (penalise bigger)

                 2nd example {"}Near Infra-red reflectance instrument
                 for the inference of the protien contents of ground
                 wheat{"} (old data, (1983, T.Fearn {"}A misuse of ridge
                 regression in the clibration of near infrared
                 relectance instrument{"}, Appl Statistics, 32, 1,
                 73-79), various techniques already tried). GP
                 {"}provide simple non-linear model that provides far
                 greater insight into the input-output model structure
                 than other non-lenear modelling techniques such as
                 neural networks{"} RMS error also better than cited in
                 literature (traditional stats and ANN).

                 3rd: recovery of contaminated transformer oil. GP
                 solution robust to measurement error.",

Genetic Programming entries for Ben McKay Mark J Willis Geoffrey W Barton

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