Design \& Implementation of Parallel Linear GP for the IBM Cell Processor

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

@InProceedings{Comte:2009:CIGPU,
  author =       "Pascal Comte",
  title =        "Design \& Implementation of Parallel Linear GP for the
                 IBM Cell Processor",
  booktitle =    "GECCO '09: Proceedings of the 11th Annual conference
                 on Genetic and evolutionary computation",
  year =         "2009",
  editor =       "Guenther Raidl and Franz Rothlauf and 
                 Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and 
                 Mauro Birattari and Clare Bates Congdon and 
                 Martin Middendorf and Christian Blum and Carlos Cotta and 
                 Peter Bosman and Joern Grahl and Joshua Knowles and 
                 David Corne and Hans-Georg Beyer and Ken Stanley and 
                 Julian F. Miller and Jano {van Hemert} and 
                 Tom Lenaerts and Marc Ebner and Jaume Bacardit and 
                 Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and 
                 Thomas Jansen and Riccardo Poli and Enrique Alba",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-60558-325-9",
  DOI =          "doi:10.1145/1569901.1596274",
  abstract =     "We present two different single-core parallel SIMD
                 linear genetic programming (LGP) systems for the IBM
                 Cell Processor on the Playstation3. Our algorithms
                 harness their computational power from the parallel
                 capabilities of the Cell Processor. We implement two
                 evolutionary algorithms and look at the classical
                 problem of symbolic regression of functions. The first
                 LGP generates a single offspring and selection from the
                 population occurs randomly. The second algorithm
                 generates two offspring and selection from the
                 population is performed using k-tournament with k = 2.
                 Mutation occurs at macro and micro levels. Both SIMD
                 instructions and register operands are subject to
                 mutation. We use a static population of 648 individuals
                 due to memory and data transfer restrictions and,
                 experiments are constrained to 300 seconds of
                 computational time. Our results indicate that both EAs
                 perform equally well though the first algorithm is
                 faster and outperforms the 2nd algorithm in some cases.
                 We speculate that the speed at which generations are
                 iterated through is significantly greater than that of
                 a typical tree-based GP and sequential linear GP.",
  notes =        "Also known as \cite{1596274}. Omitted when CD was
                 pressed. CIGPU-2009

                 GECCO-2009 A joint meeting of the eighteenth
                 international conference on genetic algorithms
                 (ICGA-2009) and the fourteenth annual genetic
                 programming conference (GP-2009).",
}

Genetic Programming entries for Pascal Comte

Citations