Large Scale Bioinformatics Data Mining with Parallel Genetic Programming on Graphics Processing Units

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InCollection{langdon:2009:pdci,
  author =       "W. B. Langdon",
  title =        "Large Scale Bioinformatics Data Mining with Parallel
                 Genetic Programming on Graphics Processing Units",
  booktitle =    "Parallel and Distributed Computational Intelligence",
  publisher =    "Springer",
  year =         "2010",
  editor =       "Francisco {Fernandez de Vega} and Erick Cantu-Paz",
  volume =       "269",
  series =       "Studies in Computational Intelligence",
  chapter =      "5",
  pages =        "113--141",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, GPU",
  isbn13 =       "978-3642106743",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2009_pdci.pdf",
  URL =          "http://www.springer.com/engineering/book/978-3-642-10674-3",
  DOI =          "doi:10.1007/978-3-642-10675-0_6",
  abstract =     "A suitable single instruction multiple data GP
                 interpreter can achieve high (Giga GPop/second)
                 performance on a SIMD GPU graphics card by
                 simultaneously running multiple diverse members of the
                 genetic programming population. SPMD dataflow
                 parallelisation is achieved because the single
                 interpreter treats the different GP programs as data.
                 On a single 128 node parallel nVidia GeForce 8800 GTX
                 GPU, the interpreter can out run a compiled approach,
                 where data parallelisation comes only by running a
                 single program at a time across multiple inputs.

                 The RapidMind GPGPU Linux C++ system has been
                 demonstrated by predicting ten year+ outcome of breast
                 cancer from a dataset containing a million inputs. NCBI
                 GEO GSE3494 contains hundreds of Affymetrix
                 \mbox{HG-U133A} and HG-U133B GeneChip biopsies.
                 Multiple GP runs each with a population of five million
                 programs winnow useful variables from the chaff at more
                 than 500 million GPops per second. Sources available
                 via
                 \href{http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/gpu_gp_2.tar.gz}
                 {FTP}.",
  notes =        "part of \cite{FernandezdeVega:pdci}",
  size =         "28 pages",
}

Genetic Programming entries for William B Langdon

Citations