By the same authors

From the same journal

Genetic Programming + Proof Search = Automatic Improvement

Research output: Contribution to journalArticle

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Genetic Programming + Proof Search = Automatic Improvement. / Kocsis, Zoltan A.; Swan, Jerry.

In: Journal of Automated Reasoning, 07.03.2017, p. 1-20.

Research output: Contribution to journalArticle

Harvard

Kocsis, ZA & Swan, J 2017, 'Genetic Programming + Proof Search = Automatic Improvement', Journal of Automated Reasoning, pp. 1-20. https://doi.org/10.1007/s10817-017-9409-5

APA

Kocsis, Z. A., & Swan, J. (2017). Genetic Programming + Proof Search = Automatic Improvement. Journal of Automated Reasoning, 1-20. https://doi.org/10.1007/s10817-017-9409-5

Vancouver

Kocsis ZA, Swan J. Genetic Programming + Proof Search = Automatic Improvement. Journal of Automated Reasoning. 2017 Mar 7;1-20. https://doi.org/10.1007/s10817-017-9409-5

Author

Kocsis, Zoltan A. ; Swan, Jerry. / Genetic Programming + Proof Search = Automatic Improvement. In: Journal of Automated Reasoning. 2017 ; pp. 1-20.

Bibtex - Download

@article{49c649b486114f7298b49b2c854b16ce,
title = "Genetic Programming + Proof Search = Automatic Improvement",
abstract = "Search Based Software Engineering techniques are emerging as important tools for software maintenance. Foremost among these is Genetic Improvement, which has historically applied the stochastic techniques of Genetic Programming to optimize pre-existing program code. Previous work in this area has not generally preserved program semantics and this article describes an alternative to the traditional mutation operators used, employing deterministic proof search in the sequent calculus to yield semantics-preserving transformations on algebraic data types. Two case studies are described, both of which are applicable to the recently-introduced `grow and graft' technique of Genetic Improvement: the first extends the expressiveness of the `grafting' phase and the second transforms the representation of a list data type to yield an asymptotic efficiency improvement.",
author = "Kocsis, {Zoltan A.} and Jerry Swan",
note = "{\textcopyright} The Author(s) 2017. ",
year = "2017",
month = mar,
day = "7",
doi = "10.1007/s10817-017-9409-5",
language = "English",
pages = "1--20",
journal = "Journal of Automated Reasoning",
issn = "0168-7433",
publisher = "Springer Netherlands",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Genetic Programming + Proof Search = Automatic Improvement

AU - Kocsis, Zoltan A.

AU - Swan, Jerry

N1 - © The Author(s) 2017.

PY - 2017/3/7

Y1 - 2017/3/7

N2 - Search Based Software Engineering techniques are emerging as important tools for software maintenance. Foremost among these is Genetic Improvement, which has historically applied the stochastic techniques of Genetic Programming to optimize pre-existing program code. Previous work in this area has not generally preserved program semantics and this article describes an alternative to the traditional mutation operators used, employing deterministic proof search in the sequent calculus to yield semantics-preserving transformations on algebraic data types. Two case studies are described, both of which are applicable to the recently-introduced `grow and graft' technique of Genetic Improvement: the first extends the expressiveness of the `grafting' phase and the second transforms the representation of a list data type to yield an asymptotic efficiency improvement.

AB - Search Based Software Engineering techniques are emerging as important tools for software maintenance. Foremost among these is Genetic Improvement, which has historically applied the stochastic techniques of Genetic Programming to optimize pre-existing program code. Previous work in this area has not generally preserved program semantics and this article describes an alternative to the traditional mutation operators used, employing deterministic proof search in the sequent calculus to yield semantics-preserving transformations on algebraic data types. Two case studies are described, both of which are applicable to the recently-introduced `grow and graft' technique of Genetic Improvement: the first extends the expressiveness of the `grafting' phase and the second transforms the representation of a list data type to yield an asymptotic efficiency improvement.

U2 - 10.1007/s10817-017-9409-5

DO - 10.1007/s10817-017-9409-5

M3 - Article

SP - 1

EP - 20

JO - Journal of Automated Reasoning

JF - Journal of Automated Reasoning

SN - 0168-7433

ER -