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Automated code generation by local search.

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Bibliographic Details
Title:	Automated code generation by local search.
Authors:	Hyde, M R¹, Burke, E K¹, Kendall, G¹
Source:	Journal of the Operational Research Society. Dec2013, Vol. 64 Issue 12, p1725-1741. 17p.
Subjects:	Computational statistics, Genetic programming, Computer programming, Genetic algorithms, Computer systems, Space
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. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
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. [ABSTRACT FROM AUTHOR]
ISSN:	01605682
DOI:	10.1057/jors.2012.149