Acceleration of grammatical evolution using graphics processing units: computational intelligence on consumer games and graphics hardware

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Pospichal:2011:GECCOcomp,
  author =       "Petr Pospichal and Eoin Murphy and Michael O'Neill and 
                 Josef Schwarz and Jiri Jaros",
  title =        "Acceleration of grammatical evolution using graphics
                 processing units: computational intelligence on
                 consumer games and graphics hardware",
  booktitle =    "GECCO 2011 Computational intelligence on consumer
                 games and graphics hardware (CIGPU)",
  year =         "2011",
  editor =       "Simon Harding and W. B. Langdon and Man Leung Wong and 
                 Garnett Wilson and Tony Lewis",
  isbn13 =       "978-1-4503-0690-4",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, GPU, CUDA, GPGPU, symbolic regression",
  pages =        "431--438",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001858.2002030",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Several papers show that symbolic regression is
                 suitable for data analysis and prediction in financial
                 markets. Grammatical Evolution (GE), a grammar-based
                 form of Genetic Programming (GP), has been successfully
                 applied in solving various tasks including symbolic
                 regression. However, often the computational effort to
                 calculate the fitness of a solution in GP can limit the
                 area of possible application and/or the extent of
                 experimentation undertaken. This paper deals with using
                 mainstream graphics processing units (GPU) for
                 acceleration of GE solving symbolic regression. GPU
                 optimisation details are discussed and the NVCC
                 compiler is analysed. We design an effective mapping of
                 the algorithm to the CUDA framework, and in so doing
                 must tackle constraints of the GPU approach, such as
                 the PCI-express bottleneck and main memory
                 transactions.

                 This is the first occasion GE has been adapted for
                 running on a GPU. We measure our implementation running
                 on one core of CPU Core i7 and GPU GTX 480 together
                 with a GE library written in JAVA, GEVA.

                 Results indicate that our algorithm offers the same
                 convergence, and it is suitable for a larger number of
                 regression points where GPU is able to reach speedups
                 of up to 39 times faster when compared to GEVA on a
                 serial CPU code written in C. In conclusion, properly
                 used, GPU can offer an interesting performance boost
                 for GE tackling symbolic regression.",
  notes =        "Two kernels: 1) selection 2) genotype-phenotype
                 mapping, fitness, crossover, mutation. One grammar for
                 problem x +x^2 + x^3 + x^4. Tries Population 2 ... 64
                 and 128 ... 2560 training cases. Comparison with PC in
                 C and Java (GEVA). No absolute speed measure given (cf.
                 \cite{langdon:2008:eurogp}).

                 Also known as \cite{2002030} Distributed on CD-ROM at
                 GECCO-2011.

                 ACM Order Number 910112.",
}

Genetic Programming entries for Petr Pospichal Eoin Murphy Michael O'Neill Josef Schwarz Jiri Jaros

Citations