Effects of constant optimization by nonlinear least squares minimization in symbolic regression

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@InProceedings{Kommenda:2013:GECCOcomp,
  author =       "Michael Kommenda and Gabriel Kronberger and 
                 Stephan Winkler and Michael Affenzeller and Stefan Wagner",
  title =        "Effects of constant optimization by nonlinear least
                 squares minimization in symbolic regression",
  booktitle =    "GECCO '13 Companion: Proceeding of the fifteenth
                 annual conference companion on Genetic and evolutionary
                 computation conference companion",
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and 
                 Thomas Bartz-Beielstein and Daniele Loiacono and 
                 Francisco Luna and Joern Mehnen and Gabriela Ochoa and 
                 Mike Preuss and Emilia Tantar and Leonardo Vanneschi and 
                 Kent McClymont and Ed Keedwell and Emma Hart and 
                 Kevin Sim and Steven Gustafson and 
                 Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and 
                 Nikolaus Hansen and Olaf Mersmann and Petr Posik and 
                 Heike Trautmann and Muhammad Iqbal and Kamran Shafi and 
                 Ryan Urbanowicz and Stefan Wagner and 
                 Michael Affenzeller and David Walker and Richard Everson and 
                 Jonathan Fieldsend and Forrest Stonedahl and 
                 William Rand and Stephen L. Smith and Stefano Cagnoni and 
                 Robert M. Patton and Gisele L. Pappa and 
                 John Woodward and Jerry Swan and Krzysztof Krawiec and 
                 Alexandru-Adrian Tantar and Peter A. N. Bosman and 
                 Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and 
                 David L. Gonzalez-Alvarez and 
                 Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and 
                 Kenneth Holladay and Tea Tusar and Boris Naujoks",
  isbn13 =       "978-1-4503-1964-5",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "1121--1128",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2464576.2482691",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In this publication a constant optimisation approach
                 for symbolic regression is introduced to separate the
                 task of finding the correct model structure from the
                 necessity to evolve the correct numerical constants. A
                 gradient-based nonlinear least squares optimisation
                 algorithm, the Levenberg-Marquardt (LM) algorithm, is
                 used for adjusting constant values in symbolic
                 expression trees during their evolution. The LM
                 algorithm depends on gradient information consisting of
                 partial derivations of the trees, which are obtained by
                 automatic differentiation.

                 The presented constant optimization approach is tested
                 on several benchmark problems and compared to a
                 standard genetic programming algorithm to show its
                 effectiveness. Although the constant optimization
                 involves an overhead regarding the execution time, the
                 achieved accuracy increases significantly as well as
                 the ability of genetic programming to learn from
                 provided data. As an example, the Pagie-1 problem could
                 be solved in 37 out of 50 test runs, whereas without
                 constant optimisation it was solved in only 10 runs.
                 Furthermore, different configurations of the constant
                 optimisation approach (number of iterations,
                 probability of applying constant optimisation) are
                 evaluated and their impact is detailed in the results
                 section.",
  notes =        "Also known as \cite{2482691} Distributed at
                 GECCO-2013.",
}

Genetic Programming entries for Michael Kommenda Gabriel Kronberger Stephan M Winkler Michael Affenzeller Stefan Wagner

Citations