An Experimental Perspective on Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{ppsn92:oReilly,
  author =       "Una-May O'Reilly and Franz Oppacher",
  title =        "An Experimental Perspective on Genetic Programming",
  booktitle =    "Parallel Problem Solving from Nature 2",
  year =         "1992",
  editor =       "R Manner and B Manderick",
  pages =        "331--340",
  address =      "Brussels, Belgium",
  month =        sep # " 28 - 30",
  publisher =    "Elsevier Science",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/ppsn92.ps.gz",
  size =         "10 pages",
  abstract =     "Genetic Programming (GP) has recently been introduced
                 by John R. Koza as a method for genetically breeding
                 populations of computer programs to solve problems. We
                 believe GP to constitute a significant extension of the
                 Genetic Algorithm (GA) research paradigm primarily
                 because it generalizes the genetic search techniques:
                 instead of looking for a solution to a specific
                 instance of a problem, GP attempts to evolve a program
                 capable of computing the solutions for any instance of
                 the problem. We have implemented a genetic programming
                 environment, GP*, that is capable of duplicating Koza`s
                 experiments. In this paper we describe a specific GP
                 experiment on the evolution of programs to sort
                 vectors, and discuss the issues that must be addressed
                 in any application of GP: the design of fitness
                 functions and test suites, and the selection of program
                 terminals and functions. Our observations point to
                 several previously unnoticed shortcomings of the GP
                 approach. We hypothesize that these shortcomings are
                 due to the fact that GP only uses a hierarchical
                 representation but does not construct its solutions in
                 an explicitly hierarchical manner.",
  notes =        "Critical of Koza's GP (nb non-ADF) {"}We conclude that
                 GP in its current form is heirarchical only with
                 respect to its representation and not with resepect to
                 its process of constructing solutions. This limits the
                 ability of GP to evolve complex programs from simple,
                 general functions, and makes the algorithm stongly
                 dependant on initial human design
                 decisions.{"}

                 Proposes SPECIALISE and DECOMPOSE operators, like
                 encapsulate and expand, but applied infrequently and
                 depending upon how the GP is going. SPECIALISE would
                 look for common code in better programs and convert
                 them to functions which cannot be disrupted by
                 crossover.

                 However: ``Regarding the specialize and decompose
                 operators, we abandoned them after very preliminary
                 work''.

                 References Ken De Jong ICGA-87

                 ",
}

Genetic Programming entries for Una-May O'Reilly Franz Oppacher

Citations