Automated code generation by local search

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@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