Speeding up the evaluation phase of GP classification algorithms on GPUs

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Cano:2011:SC,
  author =       "Alberto Cano and Amelia Zafra and Sebastian Ventura",
  title =        "Speeding up the evaluation phase of GP classification
                 algorithms on GPUs",
  journal =      "Soft Computing - A Fusion of Foundations,
                 Methodologies and Applications",
  year =         "2012",
  volume =       "16",
  number =       "2",
  pages =        "187--202",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, GPU, Computer
                 Science",
  publisher =    "Springer Berlin / Heidelberg",
  ISSN =         "1432-7643",
  DOI =          "doi:10.1007/s00500-011-0713-4",
  size =         "16 pages",
  abstract =     "The efficiency of evolutionary algorithms has become a
                 studied problem since it is one of the major weaknesses
                 in these algorithms. Specifically, when these
                 algorithms are employed for the classification task,
                 the computational time required by them grows
                 excessively as the problem complexity increases. This
                 paper proposes an efficient scalable and massively
                 parallel evaluation model using the NVIDIA CUDA GPU
                 programming model to speed up the fitness calculation
                 phase and greatly reduce the computational time.
                 Experimental results show that our model significantly
                 reduces the computational time compared to the
                 sequential approach, reaching a speedup of up to 820
                 times. Moreover, the model is able to scale to multiple
                 GPU devices and can be easily extended to any
                 evolutionary algorithm.",
  notes =        "No absolute speed measure given (cf.
                 \cite{langdon:2008:eurogp}). UCI: Iris, New-thyroid,
                 Ecoli, Contraceptive, Thyroid, Penbased, Shuttle,
                 Connect-4, KDDcup, Poker. GTX 285, two GTX 480. 64-bit
                 Linux Ubuntu.

                 execution time was reduced from 30 hours to 2
                 minutes.",
  affiliation =  "Department of Computing and Numerical Analysis,
                 University of Cordoba, 14071 Cordoba, Spain",
}

Genetic Programming entries for Alberto Cano Rojas Amelia Zafra Gomez Sebastian Ventura

Citations