Guiding function set selection in genetic programming based on fitness landscape analysis

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Nguyen:2013:GECCOcomp,

• author = "Quang Uy Nguyen and Cong Doan Truong and Xuan Hoai Nguyen and Michael O'Neill",
• title = "Guiding function set selection in genetic programming based on fitness landscape analysis",
• 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 = "149--150",
• month = "6-10 " # jul,
• organisation = "SIGEVO",
• address = "Amsterdam, The Netherlands",
• URL = "https://www.lri.fr/~hansen/proceedings/2013/GECCO/companion/p149.pdf",
• DOI = "doi:10.1145/2464576.2466800",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "This paper attempts to provide a guideline for function set selection based on fitness landscape analysis. We used two well-known techniques, autocorrelation function and information content, to analyse the fitness landscape of each function set. We tested these methods on a large number of real-valued symbolic regression problems and the experimental results showed that there is a strong relationship between autocorrelation function value and the performance of a function set. Therefore, autocorrelation function can be used as a good indicator for selecting an appropriate function set for a problem.",
• notes = "Also known as \cite{2466800} Distributed at GECCO-2013.",

}

Genetic Programming entries for Quang Uy Nguyen Cong Doan Truong Nguyen Xuan Hoai Michael O'Neill

Citations