Self-focusing genetic programming for software optimisation
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{Cody-Kenny:2013:GECCOcomp,
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author = "Brendan Cody-Kenny and Stephen Barrett",
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title = "Self-focusing genetic programming for software
optimisation",
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booktitle = "GECCO '13 Companion: Proceeding of the fifteenth
annual conference companion on Genetic and evolutionary
computation conference companion",
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year = "2013",
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editor = "Christian Blum and Enrique Alba and
Thomas Bartz-Beielstein and Daniele Loiacono and
Francisco Luna and Joern Mehnen and Gabriela Ochoa and
Mike Preuss and Emilia Tantar and Leonardo Vanneschi and
Kent McClymont and Ed Keedwell and Emma Hart and
Kevin Sim and Steven Gustafson and
Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and
Nikolaus Hansen and Olaf Mersmann and Petr Posik and
Heike Trautmann and Muhammad Iqbal and Kamran Shafi and
Ryan Urbanowicz and Stefan Wagner and
Michael Affenzeller and David Walker and Richard Everson and
Jonathan Fieldsend and Forrest Stonedahl and
William Rand and Stephen L. Smith and Stefano Cagnoni and
Robert M. Patton and Gisele L. Pappa and
John Woodward and Jerry Swan and Krzysztof Krawiec and
Alexandru-Adrian Tantar and Peter A. N. Bosman and
Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and
David L. Gonzalez-Alvarez and
Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and
Kenneth Holladay and Tea Tusar and Boris Naujoks",
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isbn13 = "978-1-4503-1964-5",
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keywords = "genetic algorithms, genetic programming",
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pages = "203--204",
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month = "6-10 " # jul,
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organisation = "SIGEVO",
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address = "Amsterdam, The Netherlands",
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DOI = "doi:10.1145/2464576.2464681",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "Approaches in the area of Search Based Software
Engineering (SBSE) have seen Genetic Programming (GP)
algorithms applied to the optimisation of software.
While the potential of GP for this task has been
demonstrated, the complexity of real-world software
code bases poses a scalability problem for its serious
application. To address this scalability problem, we
inspect a form of GP which incorporates a mechanism to
focus operators to relevant locations within a program
code base. When creating offspring individuals, we
introduce operator node selection bias by allocating
values to nodes within an individual. Offspring values
are inherited and updated when a difference in
behaviour between offspring and parent is found. We
argue that this approach may scale to find optimal
solutions in more complex code bases under further
development.",
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notes = "Also known as \cite{2464681} Distributed at
GECCO-2013.",
- }
Genetic Programming entries for
Brendan Cody-Kenny
Stephen Barrett
Citations