TensorFlow Enabled Genetic Programming

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

  author =       "Kai Staats and Edward Pantridge and Marco Cavaglia and 
                 Iurii Milovanov and Arun Aniyan",
  title =        "{TensorFlow} Enabled Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "1872--1879",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3067695.3084216",
  DOI =          "doi:10.1145/3067695.3084216",
  acmid =        "3084216",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation, gpu, machine learning, multicore,
                 parallel, tensorflow, vectorized",
  month =        "15-19 " # jul,
  abstract =     "Genetic Programming, a kind of evolutionary
                 computation and machine learning algorithm, is shown to
                 benefit significantly from the application of
                 vectorized data and the TensorFlow numerical
                 computation library on both CPU and GPU architectures.
                 The open source, Python Karoo GP is employed for a
                 series of 190 tests across 6 platforms, with real-world
                 datasets ranging from 18 to 5.5M data points. This body
                 of tests demonstrates that datasets measured in tens
                 and hundreds of data points see 2--15x improvement when
                 moving from the scalar/SymPy configuration to the
                 vector/TensorFlow configuration, with a single core
                 performing on par or better than multiple CPU cores and
                 CPUs. A dataset composed of 90,000 data points
                 demonstrates a single vector/TensorFlow CPU core
                 performing 875x better than 40 scalar/Sympy CPU cores.
                 And a dataset containing 5.5M data points sees GPU
                 configurations out-performing CPU configurations on
                 average by 1.3x.",
  notes =        "Also known as \cite{Staats:2017:TEG:3067695.3084216}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",

Genetic Programming entries for Kai Staats Edward R Pantridge Marco Cavaglia Iurii Milovanov Arun Aniyan