Accelerating convergence in cartesian genetic programming by using a new genetic operator
Created by W.Langdon from
gp-bibliography.bib Revision:1.8323
- @InProceedings{Meier:2013:GECCO,
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author = "Andreas Meier and Mark Gonter and Rudolf Kruse",
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title = "Accelerating convergence in cartesian genetic
programming by using a new genetic operator",
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booktitle = "GECCO '13: Proceeding of the fifteenth annual
conference on Genetic and evolutionary computation
conference",
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year = "2013",
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editor = "Christian Blum and Enrique Alba and Anne Auger and
Jaume Bacardit and Josh Bongard and Juergen Branke and
Nicolas Bredeche and Dimo Brockhoff and
Francisco Chicano and Alan Dorin and Rene Doursat and
Aniko Ekart and Tobias Friedrich and Mario Giacobini and
Mark Harman and Hitoshi Iba and Christian Igel and
Thomas Jansen and Tim Kovacs and Taras Kowaliw and
Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and
John McCall and Alberto Moraglio and
Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and
Gustavo Olague and Yew-Soon Ong and
Michael E. Palmer and Gisele Lobo Pappa and
Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and
Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and
Daniel Tauritz and Leonardo Vanneschi",
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pages = "981--988",
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month = "6-10 " # jul,
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organisation = "SIGEVO",
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address = "Amsterdam, The Netherlands",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, cartesian
genetic programming, optimization, genetic operator",
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isbn13 = "978-1-4503-1963-8",
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URL = "
http://www.cmap.polytechnique.fr/~nikolaus.hansen/proceedings/2013/GECCO/proceedings/p981.pdf",
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DOI = "
doi:10.1145/2463372.2463481",
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size = "8 pages",
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abstract = "Genetic programming algorithms seek to find
interpretable and good solutions for problems which are
difficult to solve analytically. For example, we plan
to use this paradigm to develop a car accident severity
prediction model for new occupant safety functions.
This complex problem will suffer from the major
disadvantage of genetic programming, which is its high
demand for computational effort to find good solutions.
A main reason for this demand is a low rate of
convergence. we introduce a new genetic operator called
forking to accelerate the rate of convergence. Our idea
is to interpret individuals dynamically as centres of
local Gaussian distributions and allow a sampling
process in these distributions when populations get too
homogeneous. We demonstrate this operator by extending
the Cartesian Genetic Programming algorithm and show
that on our examples convergence is accelerated by over
50percent on average. We finish this paper with giving
hints about parametrisation of the forking operator for
other problems.",
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notes = "Also known as \cite{2463481} GECCO-2013 A joint
meeting of the twenty second international conference
on genetic algorithms (ICGA-2013) and the eighteenth
annual genetic programming conference (GP-2013)",
- }
Genetic Programming entries for
Andreas Meier
Mark Gonter
Rudolf Kruse
Citations