Parallel Distributed Genetic Programming

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

  author =       "Riccardo Poli",
  title =        "Parallel Distributed Genetic Programming",
  booktitle =    "New Ideas in Optimization",
  publisher =    "McGraw-Hill",
  year =         "1999",
  editor =       "David Corne and Marco Dorigo and Fred Glover",
  series =       "Advanced Topics in Computer Science",
  chapter =      "27",
  pages =        "403--431",
  address =      "Maidenhead, Berkshire, England",
  keywords =     "genetic algorithms, genetic programming, PDGP",
  ISBN =         "0-07-709506-5",
  URL =          "",
  URL =          "",
  abstract =     "This chapter describes Parallel Distributed Genetic
                 Programming (PDGP), a form of Genetic Programming (GP)
                 which is suitable for the development of programs with
                 a high degree of parallelism and an efficient and
                 effective reuse of partial results. Programs are
                 represented in PDGP as graphs with nodes representing
                 functions and terminals, and links representing the
                 flow of control and results. In the simplest form of
                 PDGP links are directed and unlabelled, in which case
                 PDGP can be considered a generalisation of standard GP.
                 However, more complex representations can be used,
                 which allow the exploration of a large space of
                 possible programs including standard tree-like
                 programs, logic networks, neural networks, recurrent
                 transition networks, finite state automata, etc. In
                 PDGP, programs are manipulated by special crossover and
                 mutation operators which guarantee the syntactic
                 correctness of the offspring. For this reason PDGP
                 search is very efficient. PDGP programs can be
  notes =        "

                 This is the most complete account of PDGP and its
                 performance so far. E.g. a symbolic regression problem
                 x^6-2*x^4+x^2 in which PDGP does 16 times better than
                 std GP and 13 times better than GP with ADFs.

                 XOR, lawnmower, sextic polynomial, encoder-decoder FSA
                 induction, NLP,",
  size =         "29 pages",

Genetic Programming entries for Riccardo Poli