Automated code generation by local search

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@Article{Hyde:2013:JORS,
  author =       "M. R. Hyde and E. K. Burke and G. Kendall",
  title =        "Automated code generation by local search",
  journal =      "Journal of the Operational Research Society",
  year =         "2013",
  volume =       "64",
  number =       "12",
  pages =        "1725--1741",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, heuristics,
                 local search",
  publisher =    "Palgrave Macmillan",
  ISSN =         "0160-5682",
  URL =          "http://dx.doi.org/10.1057/jors.2012.149",
  DOI =          "doi:10.1057/jors.2012.149",
  size =         "17 pages",
  abstract =     "There are many successful evolutionary computation
                 techniques for automatic program generation, with the
                 best known, perhaps, being genetic programming. Genetic
                 programming has obtained human competitive results,
                 even infringing on patented inventions. The majority of
                 the scientific literature on automatic program
                 generation employs such population-based search
                 approaches, to allow a computer system to search a
                 space of programs. In this paper, we present an
                 alternative approach based on local search. There are
                 many local search methodologies that allow successful
                 search of a solution space, based on maintaining a
                 single incumbent solution and searching its
                 neighbourhood. However, use of these methodologies in
                 searching a space of programs has not yet been
                 systematically investigated. The contribution of this
                 paper is to show that a local search of programs can be
                 more successful at automatic program generation than
                 current nature inspired evolutionary computation
                 methodologies.",
  notes =        "Also known as \cite{Hyde2013}",
}

Genetic Programming entries for Matthew R Hyde Edmund Burke Graham Kendall

Citations