Evolving a digital multiplier with the pushgp genetic programming system
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Helmuth:2013:GECCOcomp,
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author = "Thomas Helmuth and Lee Spector",
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title = "Evolving a digital multiplier with the pushgp genetic
programming system",
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booktitle = "GECCO '13 Companion: Proceeding of the fifteenth
annual conference companion on Genetic and evolutionary
computation conference companion",
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year = "2013",
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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",
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isbn13 = "978-1-4503-1964-5",
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keywords = "genetic algorithms, genetic programming",
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pages = "1627--1634",
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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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DOI = "doi:10.1145/2464576.2466814",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "A recent article on benchmark problems for genetic
programming suggested that researchers focus attention
on the digital multiplier problem, also known as the
multiple output multiplier problem, in part because it
is scalable and in part because the requirement of
multiple outputs presents challenges for some forms of
genetic programming [20]. Here we demonstrate the
application of stack-based genetic programming to the
digital multiplier problem using the PushGP genetic
programming system, which evolves programs expressed in
the stack-based Push programming language. We
demonstrate the use of output instructions and argue
that they provide a natural mechanism for producing
multiple outputs in a stack-based genetic programming
context. We also show how two recent developments in
PushGP dramatically improve the performance of the
system on the digital multiplier problem. These
developments are the ULTRA genetic operator, which
produces offspring via Uniform Linear Transformation
with Repair and Alternation [12], and lexicase
selection, which selects parents according to
performance on cases considered sequentially in random
order [11]. Our results using these techniques show not
only their utility, but also the utility of the digital
multiplier problem as a benchmark problem for genetic
programming research. The results also demonstrate the
exibility of stack-based genetic programming for
solving problems with multiple outputs and for serving
as a platform for experimentation with new genetic
programming techniques.",
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notes = "Also known as \cite{2466814} Distributed at
GECCO-2013.",
- }
Genetic Programming entries for
Thomas Helmuth
Lee Spector
Citations