An evolutionary methodology for automatic design of finite state machines

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Colmenar:2013:GECCOcomp,

• 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 GECCO-2013.",

}

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

Citations