Genetic programming and cognitive models   [GP]

by

Dallaway, R.

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Info: 1993
Keywords:genetic algorithms, genetic programming
Abstract:
Genetic programming (GP) [GP] is a general purpose method for evolving symbolic computer programs (e.g. Lisp code). Concepts from genetic algorithms [GA] are used to evolve a population of initially random programs so that they are able to solve the problem at hand. This paper describes genetic programming [GP] and discuss the usefulness of the method for building cognitive models. Although it appears that an arbitrary fit to the training examples will be evolved, it is shown that GP can be constrained to produce small, general programs.
Notes:
symbolic regression of 2.719x^2 + 3.14161x from 20 random points, parsimony pressure used
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BibTex:
@TechReport{dallaway:1993:GPcm,
  author =       "Richard Dallaway",
  title =        "Genetic programming and cognitive models",
  institution =  "School of Cognitive \& Computing Sciences, University
                 of Sussex,",
  year =         "1993",
  number =       "CSRP 300",
  address =      "Brighton, UK",
  note =         "In: Brook \& Arvanitis, eds., 1993 The Sixth White
                 House Papers: Graduate Research in the Cognitive \&
                 Computing Sciences at Sussex",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.dallaway.demon.co.uk/evolution/evocog.html",
  abstract =     "Genetic programming (GP) is a general purpose method
                 for evolving symbolic computer programs (e.g. Lisp
                 code). Concepts from genetic algorithms are used to
                 evolve a population of initially random programs so
                 that they are able to solve the problem at hand. This
                 paper describes genetic programming and discuss the
                 usefulness of the method for building cognitive models.
                 Although it appears that an arbitrary fit to the
                 training examples will be evolved, it is shown that GP
                 can be constrained to produce small, general
                 programs.",
  notes =        "symbolic regression of 2.719x^2 + 3.14161x from 20
                 random points, parsimony pressure used",
}