Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InCollection{Awange2016,
  author =       "Joseph L. Awange and Bela Palancz",
  title =        "Symbolic Regression",
  booktitle =    "Geospatial Algebraic Computations: Theory and
                 Applications",
  publisher =    "Springer",
  year =         "2016",
  chapter =      "11",
  pages =        "203--216",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-25465-4",
  DOI =          "doi:10.1007/978-3-319-25465-4_11",
  abstract =     "Symbolic regression (SR) is the process of determining
                 the symbolic function, which describes a data
                 set-effectively developing an analytic model, which
                 summarizes the data and is useful for predicting
                 response behaviours as well as facilitating human
                 insight and understanding. The symbolic regression
                 approach adopted herein is based upon genetic
                 programming wherein a population of functions are
                 allowed to breed and mutate with the genetic
                 propagation into subsequent generations based upon a
                 survival-of-the-fittest criteria. Amazingly, this works
                 and, although computationally intensive, summary
                 solutions may be reasonably discovered using current
                 laptop and desktop computers.",
  notes =        "Author Affiliations

                 Curtin University, Perth, West Australia,
                 Australia

                 Budapest University of Technology and Economics,
                 Budapest, Hungary",
}

Genetic Programming entries for Joseph L Awange Bela Palancz

Citations