GP on SPMD parallel Graphics Hardware for mega Bioinformatics Data Mining

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{langdon:2008:SC,

• author = "W. B. Langdon and A. P. Harrison",
• title = "{GP} on {SPMD} parallel Graphics Hardware for mega Bioinformatics Data Mining",
• journal = "Soft Computing",
• year = "2008",
• volume = "12",
• number = "12",
• pages = "1169--1183",
• month = oct,
• note = "Special Issue on Distributed Bioinspired Algorithms",
• keywords = "genetic algorithms, genetic programming, breast cancer, decorin, C17orf81, S-adenosylhomocysteine hydrolase, fibulin 1, Lance Miller's Uppsala GEO GSE3494 tumour biopsy, Affymetrix HG-U133A, HG-U133B, data mining, consumer graphics hardware, GPU, Graphics Processing Unit, SIMD, parallel computing, genetic programming, soft computing, evolutionary algorithm, RapidMind Ubuntu GCC C++",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2008_SC.pdf",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2008_SC.ps.gz",
• DOI = "doi:10.1007/s00500-008-0296-x",
• size = "11 pages",
• abstract = "We demonstrate a SIMD C++ genetic programming system on a single 128 node parallel nVidia GeForce 8800 GTX GPU under RapidMind's GPGPU Linux software by predicting ten year+ outcome of breast cancer from a dataset containing a million inputs. NCBI GEO GSE3494 contains hundreds of Affymetrix HG-U133A and HG-U133B GeneChip biopsies. Multiple GP runs each with a population of 5 million programs winnow useful variables from the chaff at more than 500 million GPops per second. Sources and dataset available.",
• notes = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/gpu_gp_2.tar.gz

  http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/GSE3494",

}

Genetic Programming entries for William B Langdon Andrew P Harrison

Citations