GPGPGPU: Evaluation of Parallelisation of Genetic Programming using GPGPU

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Jinhan Kim and Junhwi Kim and Shin Yoo",
  title =        "{GPGPGPU}: Evaluation of Parallelisation of Genetic
                 Programming using GPGPU",
  booktitle =    "Proceedings of the 9th International Symposium on
                 Search Based Software Engineering, SSBSE 2017",
  year =         "2017",
  editor =       "Tim Menzies and Justyna Petke",
  volume =       "10452",
  series =       "LNCS",
  pages =        "137--142",
  address =      "Paderborn, Germany",
  month =        sep # " 9-11",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, GPU",
  isbn13 =       "978-3-319-66299-2",
  DOI =          "doi:10.1007/978-3-319-66299-2_11",
  size =         "6 pages",
  abstract =     "We evaluate different approaches towards
                 parallelisation of Genetic Programming (GP) using
                 General Purpose Computing on Graphics Processor Units
                 (GPGPU). Unlike Genetic Algorithms, which uses a single
                 or a fixed number of fitness functions, GP has to
                 evaluate a diverse population of programs. Since GPGPU
                 is based on the Single Instruction Multiple Data (SIMD)
                 architecture, parallelisation of GP using GPGPU allows
                 multiple approaches. We study three different
                 parallelisation approaches: kernel per individual,
                 kernel per generation, and kernel interpreter. The
                 results of the empirical study using a widely studied
                 symbolic regression benchmark show that no single
                 approach is the best: the decision about
                 parallelisation approach has to consider the trade-off
                 between the compilation and the execution overhead of
                 GPU kernels.",
  notes =        "Short Papers Co-located with
                 FSE/ESEC 2017",

Genetic Programming entries for Jinhan Kim Junhwi Kim Shin Yoo