An evolutionary methodology for automatic design of finite state machines

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "J. Manuel Colmenar and Alfredo Cuesta-Infante and 
                 Jose L. Risco-Martin and J. Ignacio Hidalgo",
  title =        "An evolutionary methodology for automatic design of
                 finite state machines",
  booktitle =    "GECCO '13 Companion: Proceeding of the fifteenth
                 annual conference companion on Genetic and evolutionary
                 computation conference companion",
  year =         "2013",
  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",
  isbn13 =       "978-1-4503-1964-5",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "139--140",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2464576.2464645",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  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.",
  notes =        "Also known as \cite{2464645} Distributed at

Genetic Programming entries for J Manuel Colmenar Alfredo Cuesta-Infante Jose L Risco-Martin Jose Ignacio Hidalgo Perez