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Automated code generation by local search.

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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]
Copyright of Journal of the Operational Research Society is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+Operational+Research+Society%22">Journal of the Operational Research Society</searchLink>. Dec2013, Vol. 64 Issue 12, p1725-1741. 17p.
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  Data: 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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  Data: <i>Copyright of Journal of the Operational Research Society is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1057/jors.2012.149
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        Text: English
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        PageCount: 17
        StartPage: 1725
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      – SubjectFull: Computational statistics
        Type: general
      – SubjectFull: Genetic programming
        Type: general
      – SubjectFull: Computer programming
        Type: general
      – SubjectFull: Genetic algorithms
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      – SubjectFull: Computer systems
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      – SubjectFull: Space
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      – TitleFull: Automated code generation by local search.
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              Text: Dec2013
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