Evolution of Graph-like Programs with Parallel Distributed Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{poli:1997:eglpPDGP,
  author =       "Riccardo Poli",
  title =        "Evolution of Graph-like Programs with Parallel
                 Distributed Genetic Programming",
  booktitle =    "Genetic Algorithms: Proceedings of the Seventh
                 International Conference",
  year =         "1997",
  editor =       "Thomas Back",
  pages =        "346--353",
  address =      "Michigan State University, East Lansing, MI, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "19-23 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-487-1",
  URL =          "http://cswww.essex.ac.uk/staff/rpoli/papers/Poli-ICGA1997-PDGP.pdf",
  URL =          "http://citeseer.ist.psu.edu/372035.html",
  size =         "8 pages",
  abstract =     "Parallel Distributed Genetic Programming (PDGP) is a
                 new form of Genetic Programming (GP) suitable for the
                 development of programs with a high degree of
                 parallelism. Programs are represented in PDGP as graphs
                 with nodes representing functions and terminals, and
                 links representing the flow of control and results. The
                 paper presents the representations, the operators and
                 the interpreters used in PDGP, and describes
                 experiments in which PDGP has been compared to standard
                 GP.",
  notes =        "ICGA-97

                 Here PDGP was firstly applied to the lawnmower problem.
                 On this problem the effort scaled up (as the size of
                 the lawn was increased) 2300 times better than Std GP
                 and it scaled up linearly rather than exponentially.
                 Also the solutions found were between 10 and 30 times
                 smaller. Then PDGP was applied to the MAX problem.
                 Again the effort scaled up linearly (and 170 times
                 better than GP) rather than exponentially.",
}

Genetic Programming entries for Riccardo Poli

Citations