An evolutionary methodology for automatic design of finite state machines
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gp-bibliography.bib Revision:1.8110
- @InProceedings{Colmenar:2013:GECCOcomp,
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author = "J. Manuel Colmenar and Alfredo Cuesta-Infante and
Jose L. Risco-Martin and J. Ignacio Hidalgo",
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title = "An evolutionary methodology for automatic design of
finite state machines",
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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 = "139--140",
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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:10.1145/2464576.2464645",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "We propose an evolutionary flow for finite state
machine inference through the cooperation of
grammatical evolution and a genetic algorithm. This
coevolution has two main advantages. First, a
high-level description of the target problem is
accepted by the flow, being easier and affordable for
system designers. Second, the designer does not need to
define a training set of input values because it is
automatically generated by the genetic algorithm at run
time. Our experiments on the sequence recogniser and
the vending machine problems obtained the FSM solution
in 99.96percent and 100percent of the optimisation
runs, respectively.",
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notes = "Also known as \cite{2464645} Distributed at
GECCO-2013.",
- }
Genetic Programming entries for
J Manuel Colmenar
Alfredo Cuesta-Infante
Jose L Risco-Martin
Jose Ignacio Hidalgo Perez
Citations