Automatic Programming in an Arbitrary Language: Evolving Programs with Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Michael O'Neill",
  title =        "Automatic Programming in an Arbitrary Language:
                 Evolving Programs with Grammatical Evolution",
  school =       "University Of Limerick",
  year =         "2001",
  address =      "Ireland",
  month =        aug,
  email =        "",
  keywords =     "genetic algorithms, genetic programming, grammatical
  URL =          "",
  size =         "163 pages",
  abstract =     "We present a novel Evolutionary Automatic Programming
                 system, Grammatical Evolution that is capable of
                 generating programs in an arbitrary language from a
                 binary string. Grammatical Evolution adopts a genotype
                 to phenotype mapping; the genotype is the raw genetic
                 material, analogous to the DNA of Molecular Biology,
                 and the phenotype the functional program that is
                 generated (the equivalent of proteins in Molecular
                 Biology). Resulting from the genotype-phenotype
                 distinction, and inspired by Molecular Biology, a
                 number of features are introduced that result in
                 benefits for Grammatical Evolution. We demonstrate
                 Grammatical Evolution's viability on a number of proof
                 of concept problems with performance on a par with, and
                 in some cases superior to Genetic Programming. An
                 analysis of the system is conducted in which we focus
                 on a number of features arising directly from the
                 genotype-phenotype distinction, namely the degenerate
                 genetic code, and the novel, wrapping operator. We
                 conclude the investigations with an analysis of the
                 effects of the genetic operator of crossover on
                 Grammatical Evolution, before detailing our conclusions
                 and outlining directions for future research.",

Genetic Programming entries for Michael O'Neill