Accelerating convergence in cartesian genetic programming by using a new genetic operator

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Andreas Meier and Mark Gonter and Rudolf Kruse",
  title =        "Accelerating convergence in cartesian genetic
                 programming by using a new genetic operator",
  booktitle =    "GECCO '13: Proceeding of the fifteenth annual
                 conference on Genetic and evolutionary computation
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and Anne Auger and 
                 Jaume Bacardit and Josh Bongard and Juergen Branke and 
                 Nicolas Bredeche and Dimo Brockhoff and 
                 Francisco Chicano and Alan Dorin and Rene Doursat and 
                 Aniko Ekart and Tobias Friedrich and Mario Giacobini and 
                 Mark Harman and Hitoshi Iba and Christian Igel and 
                 Thomas Jansen and Tim Kovacs and Taras Kowaliw and 
                 Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and 
                 John McCall and Alberto Moraglio and 
                 Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and 
                 Gustavo Olague and Yew-Soon Ong and 
                 Michael E. Palmer and Gisele Lobo Pappa and 
                 Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and 
                 Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and 
                 Daniel Tauritz and Leonardo Vanneschi",
  isbn13 =       "978-1-4503-1963-8",
  pages =        "981--988",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2463372.2463481",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Genetic programming algorithms seek to find
                 interpretable and good solutions for problems which are
                 difficult to solve analytically. For example, we plan
                 to use this paradigm to develop a car accident severity
                 prediction model for new occupant safety functions.
                 This complex problem will suffer from the major
                 disadvantage of genetic programming, which is its high
                 demand for computational effort to find good solutions.
                 A main reason for this demand is a low rate of
                 convergence. In this paper, we introduce a new genetic
                 operator called forking to accelerate the rate of
                 convergence. Our idea is to interpret individuals
                 dynamically as centres of local Gaussian distributions
                 and allow a sampling process in these distributions
                 when populations get too homogeneous. We demonstrate
                 this operator by extending the Cartesian Genetic
                 Programming algorithm and show that on our examples
                 convergence is accelerated by over 50percent on
                 average. We finish this paper with giving hints about
                 parametrisation of the forking operator for other
  notes =        "Also known as \cite{2463481} GECCO-2013 A joint
                 meeting of the twenty second international conference
                 on genetic algorithms (ICGA-2013) and the eighteenth
                 annual genetic programming conference (GP-2013)",

Genetic Programming entries for Andreas Meier Mark Gonter Rudolf Kruse