Using a Tree Structured Genetic Algorithm to Perform Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.4067

  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",
  DOI =          "doi:10.1049/cp:19951096",
  size =         "6 pages",
  abstract =     "In this contribution a tree structured genetic
                 algorithm is described. The algorithm is used to
                 generate non-linear models from process input-output
                 data. Three examples are used to 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 correlation 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'. Elitist. Pop size 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 weighted according to its size'
                 (penalise bigger) Anti-bloat

                 2nd example 'Near Infra-red reflectance instrument for
                 the inference of the protein contents of ground wheat'
                 (old data, (1983, T.Fearn 'A misuse of ridge regression
                 in the calibration of near infrared reflectance
                 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-linear
                 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