Automatic Evolution of Parallel Recursive Programs

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Gopinath Chennupati and R. Muhammad Atif Azad and 
                 Conor Ryan",
  title =        "Automatic Evolution of Parallel Recursive Programs",
  booktitle =    "18th European Conference on Genetic Programming",
  year =         "2015",
  editor =       "Penousal Machado and Malcolm I. Heywood and 
                 James McDermott and Mauro Castelli and 
                 Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim",
  series =       "LNCS",
  volume =       "9025",
  publisher =    "Springer",
  pages =        "167--178",
  address =      "Copenhagen",
  month =        "8-10 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution, GPU, Automatic Parallelization, Recursion,
                 Program Synthesis, OpenMP, Evolutionary
                 Parallelization: Poster",
  isbn13 =       "978-3-319-16500-4",
  DOI =          "doi:10.1007/978-3-319-16501-1_14",
  size =         "12 pages",
  abstract =     "Writing recursive programs for fine-grained task-level
                 execution on parallel architectures, such as the
                 current generation of multi-core machines, often
                 require the application of skilled parallelization
                 knowledge to fully realize the potential of the
                 hardware. This paper automates the process by using
                 Grammatical Evolution (GE) to exploit the multi-cores
                 through the evolution of natively parallel programs. We
                 present Multi-core Grammatical Evolution (MCGE-II),
                 which employs GE and OpenMP specific pragmatic
                 information to automatically evolve task-level parallel
                 recursive programs. MCGE-II is evaluated on six
                 recursive C programs, and we show that it solves each
                 of them using parallel code. We further show that
                 MCGE-II significantly decreases the parallel
                 computational effort as the number of cores increase,
                 when tested on an Intel processor.",
  notes =        "Part of \cite{Machado:2015:GP} EuroGP'2015 held in
                 conjunction with EvoCOP2015, EvoMusArt2015 and

Genetic Programming entries for Gopinath Chennupati R Muhammad Atif Azad Conor Ryan