Symbolic Regression for Knowledge Discovery -- Bloat, Overfitting, and Variable Interaction Networks

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

@Book{Kronberger:book,
  author =       "Gabriel Kronberger",
  title =        "Symbolic Regression for Knowledge Discovery -- Bloat,
                 Overfitting, and Variable Interaction Networks",
  publisher =    "Trauner Verlag+Buchservice GmbH",
  year =         "2011",
  number =       "64",
  series =       "Johannes Kepler University, Linz, Reihe C",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-85499-875-4",
  URL =          "https://www.amazon.co.uk/Symbolic-Regression-Knowledge-Discovery-Overfitting/dp/3854998759",
  broken =       "http://www.trauner.at/buchdetail.aspx?artnr=20134641&unterkat=30",
  abstract =     "This work describes an approach for data analysis
                 based on symbolic regression and genetic programming,
                 that produces an overall view of the dependencies of
                 all variables of a system. The identified dependencies
                 are represented in form of a variable interaction
                 network.

                 In the first part of this work, this approach is
                 described in detail. Important issues are the
                 prevention of bloat and overfitting, the simplification
                 of models, and the identification of relevant input
                 variables. In this context, different methods for bloat
                 control are presented and compared. In addition, a
                 novel way to detect and reduce over fitting is
                 presented and analysed.

                 The second part of this work demonstrates how
                 comprehensive symbolic regression can be applied for
                 analysis of real-world systems. Variable interaction
                 networks for a blast furnace process and an industrial
                 chemical process are presented and discussed.
                 Additionally, the same approach is also applied on an
                 economic data set to identify macro-economic
                 dependencies.",
  notes =        "Described in
                 http://www.sigevolution.org/issues/pdf/SIGEVOlution0504.pdf
                 on page 25",
  size =         "214 pages. See also \cite{Kronberger:thesis}",
}

Genetic Programming entries for Gabriel Kronberger

Citations