Function optimization using cartesian genetic programming

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

@InProceedings{Miller:2013:GECCOcomp,

• author = "Julian F. Miller and Maktuba Mohid",
• title = "Function optimization using cartesian genetic programming",
• 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 = "147--148",
• month = "6-10 " # jul,
• organisation = "SIGEVO",
• address = "Amsterdam, The Netherlands",
• DOI = "doi:10.1145/2464576.2464646",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "In function optimisation one tries to find a vector of real numbers that optimises a complex multi-modal fitness function. Although evolutionary algorithms have been used extensively to solve such problems, genetic programming has not. In this paper, we show how Cartesian Genetic Programming can be readily applied to such problems. The technique can successfully find many optima in a standard suite of benchmark functions. The work opens up new avenues of research in the application of genetic programming and also offers an extensive set of highly developed benchmarks that could be used to compare the effectiveness of different GP methodologies.",
• notes = "Also known as \cite{2464646} Distributed at GECCO-2013.",

}

Genetic Programming entries for Julian F Miller Maktuba Mohid

Citations