GPU-parallel subtree interpreter for genetic programming

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

@InProceedings{Cano:2014:GECCO,
  author =       "Alberto Cano and Sebastian Ventura",
  title =        "GPU-parallel subtree interpreter for genetic
                 programming",
  booktitle =    "GECCO '14: Proceedings of the 2014 conference on
                 Genetic and evolutionary computation",
  year =         "2014",
  editor =       "Christian Igel and Dirk V. Arnold and 
                 Christian Gagne and Elena Popovici and Anne Auger and 
                 Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and 
                 Kalyanmoy Deb and Benjamin Doerr and James Foster and 
                 Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and 
                 Hitoshi Iba and Christian Jacob and Thomas Jansen and 
                 Yaochu Jin and Marouane Kessentini and 
                 Joshua D. Knowles and William B. Langdon and Pedro Larranaga and 
                 Sean Luke and Gabriel Luque and John A. W. McCall and 
                 Marco A. {Montes de Oca} and Alison Motsinger-Reif and 
                 Yew Soon Ong and Michael Palmer and 
                 Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and 
                 Guenther Ruhe and Tom Schaul and Thomas Schmickl and 
                 Bernhard Sendhoff and Kenneth O. Stanley and 
                 Thomas Stuetzle and Dirk Thierens and Julian Togelius and 
                 Carsten Witt and Christine Zarges",
  isbn13 =       "978-1-4503-2662-9",
  pages =        "887--894",
  keywords =     "genetic algorithms, genetic programming, GPU",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "http://doi.acm.org/10.1145/2576768.2598272",
  DOI =          "doi:10.1145/2576768.2598272",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Genetic Programming (GP) is a computationally
                 intensive technique but its nature is embarrassingly
                 parallel. Graphic Processing Units (GPUs) are many-core
                 architectures which have been widely employed to speed
                 up the evaluation of GP. In recent years, many works
                 have shown the high performance and efficiency of GPUs
                 on evaluating both the individuals and the fitness
                 cases in parallel. These approaches are known as
                 population parallel and data parallel. This paper
                 presents a parallel GP interpreter which extends these
                 approaches and adds a new parallelisation level based
                 on the concurrent evaluation of the individual's
                 subtrees. A GP individual defined by a tree structure
                 with nodes and branches comprises different depth
                 levels in which there are independent subtrees which
                 can be evaluated concurrently. Threads can cooperate to
                 evaluate different subtrees and share the results via
                 GPU's shared memory. The experimental results show the
                 better performance of the proposal in terms of the GP
                 operations per second (GPops/s) that the GP interpreter
                 is capable of processing, achieving up to 21 billion
                 GPops/s using a NVIDIA 480 GPU. However, some issues
                 raised due to limitations of currently available
                 hardware are to be overcome by the dynamic
                 parallelisation capabilities of the next generation of
                 GPUs.",
  notes =        "Also known as \cite{2598272} GECCO-2014 A joint
                 meeting of the twenty third international conference on
                 genetic algorithms (ICGA-2014) and the nineteenth
                 annual genetic programming conference (GP-2014)",
}

Genetic Programming entries for Alberto Cano Rojas Sebastian Ventura

Citations