Function optimization using cartesian genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @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