Multi-Objective Improvement of Software using Co-evolution and Smart Seeding
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{ArcuriWCY08,
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author = "Andrea Arcuri and David Robert White and
John Clark and Xin Yao",
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title = "Multi-Objective Improvement of Software using
Co-evolution and Smart Seeding",
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booktitle = "Proceedings of the 7th International Conference on
Simulated Evolution And Learning (SEAL '08)",
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year = "2008",
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editor = "Xiaodong Li and Michael Kirley and Mengjie Zhang and
David G. Green and Victor Ciesielski and
Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and
Kalyanmoy Deb and Kay Chen Tan and
J{\"u}rgen Branke and Yuhui Shi",
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volume = "5361",
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series = "Lecture Notes in Computer Science",
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pages = "61--70",
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address = "Melbourne, Australia",
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month = dec # " 7-10",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, SBSE",
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bibsource = "http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/repository.html",
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isbn13 = "978-3-540-89693-7",
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DOI = "doi:10.1007/978-3-540-89694-4_7",
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size = "10 pages",
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abstract = "Optimising non-functional properties of software is an
important part of the implementation process. One such
property is execution time, and compilers target a
reduction in execution time using a variety of
optimisation techniques. Compiler optimisation is not
always able to produce semantically equivalent
alternatives that improve execution times, even if such
alternatives are known to exist. Often, this is due to
the local nature of such optimisations. In this paper
we present a novel framework for optimising existing
software using a hybrid of evolutionary optimisation
techniques. Given as input the implementation of a
program or function, we use Genetic Programming to
evolve a new semantically equivalent version, optimised
to reduce execution time subject to a given probability
distribution of inputs. We employ a co-evolved
population of test cases to encourage the preservation
of the program's semantics, and exploit the original
program through seeding of the population in order to
focus the search. We carry out experiments to identify
the important factors in maximising efficiency gains.
Although in this work we have optimised execution time,
other non-functional criteria could be optimised in a
similar manner.",
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notes = "Also known as \cite{DBLP:conf/seal/ArcuriWCY08}",
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bibsource = "DBLP, http://dblp.uni-trier.de",
- }
Genetic Programming entries for
Andrea Arcuri
David Robert White
John A Clark
Xin Yao
Citations