Evolving a digital multiplier with the pushgp genetic programming system

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Helmuth:2013:GECCOcomp,
  author =       "Thomas Helmuth and Lee Spector",
  title =        "Evolving a digital multiplier with the pushgp genetic
                 programming system",
  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 =        "1627--1634",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2464576.2466814",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  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.",
  notes =        "Also known as \cite{2466814} Distributed at
                 GECCO-2013.",
}

Genetic Programming entries for Thomas Helmuth Lee Spector

Citations