Faster GPU Based Genetic Programming Using A Two Dimensional Stack

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  title =        "Faster {GPU} Based Genetic Programming Using {A} Two
                 Dimensional Stack",
  author =       "Darren M. Chitty",
  howpublished = "ArXiv",
  year =         "2016",
  keywords =     "genetic algorithms, genetic programming",
  bibdate =      "2016-02-01",
  bibsource =    "DBLP,
  URL =          "",
  abstract =     "Genetic Programming (GP) is a computationally
                 intensive technique which also has a high degree of
                 natural parallelism. Parallel computing architectures
                 have become commonplace especially with regards
                 Graphics Processing Units (GPU). Hence, versions of GP
                 have been implemented that use these highly parallel
                 computing platforms enabling significant gains in the
                 computational speed of GP to be achieved. However,
                 recently a two dimensional stack approach to GP using a
                 multi-core CPU also demonstrated considerable
                 performance gains. Indeed, performances equivalent to
                 or exceeding that achieved by a GPU were demonstrated.
                 This paper will demonstrate that a similar two
                 dimensional stack approach can also be applied to a GPU
                 based approach to GP to better exploit the underlying
                 technology. Performance gains are achieved over a
                 standard single dimensional stack approach when using a
                 GPU. Overall, a peak computational speed of over 55
                 billion Genetic Programming Operations per Second are
                 observed, a two fold improvement over the best GPU
                 based single dimensional stack approach from the

Genetic Programming entries for Darren M Chitty