Segment-based genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{AL-Madi:2013:GECCOcomp,
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author = "Nailah Al-Madi and Simone A. Ludwig",
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title = "Segment-based genetic programming",
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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 = "133--134",
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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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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/Segment-Based_Genetic_Programming.pdf",
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DOI = "doi:10.1145/2464576.2464648",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "Genetic Programming (GP) is one of the successful
evolutionary computation techniques applied to solve
classification problems, by searching for the best
classification model applying the fitness evaluation.
The fitness evaluation process greatly impacts the
overall execution time of GP and is therefore the focus
of this research study. This paper proposes a
segment-based GP (SegGP) technique that reduces the
execution time of GP by partitioning the dataset into
segments, and using the segments in the fitness
evaluation process. Experiments were done using four
datasets and the results show that SegGP can obtain
higher or similar accuracy results in shorter execution
time compared to standard GP.",
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notes = "Also known as \cite{2464648} Distributed at
GECCO-2013.",
- }
Genetic Programming entries for
Nailah Al-Madi
Simone A Ludwig
Citations