Evolving Multi-Line Compilable C Programs

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

@InProceedings{oneill:1999:em-lcCp,
  author =       "Michael O'Neill and Conor Ryan",
  title =        "Evolving Multi-Line Compilable {C} Programs",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and 
                 William B. Langdon and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "83--92",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution",
  ISBN =         "3-540-65899-8",
  URL =          "http://ncra.ucd.ie/papers/eurogp99.ps.gz",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=83",
  DOI =          "doi:10.1007/3-540-48885-5_7",
  abstract =     "We describe a Genetic Algorithm called Grammatical
                 Evolution (GE) that can evolve complete programs in an
                 arbitrary language using a variable length linear
                 genome. The binary genome determines which production
                 rules in a Backus Naur Form grammar definition are used
                 in a genotype to phenotype mapping process to a
                 program. Expressions and programs of arbitrary
                 complexity may be evolved using this system.

                 Since first describing this system, GE has been applied
                 to other problem domains, and during this time GE has
                 undergone some evolution. This paper serves to report
                 these changes, and also describes how we evolved
                 multi-line C-code to solve a version of the Santa Fe
                 Ant Trail. The results obtained are then compared to
                 results produced by Genetic Programming, and it is
                 found that GE outperforms GP on this problem.",
  notes =        "EuroGP'99, part of \cite{poli:1999:GP}

                 Chromosome is binary string. Interpretted as
                 Backus-Naur formal grammar. Rules of grammar are
                 prespecified to yeild very limited C program.
                 Demonstrated on modified Santa Fe trail Ant problem.",
}

Genetic Programming entries for Michael O'Neill Conor Ryan

Citations