Evolution of Recursive Transistion Networks for Natural Language Recognition with Parallel Distributed Genetic Programming

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

@InProceedings{poli:1997:RTN,
  author =       "Riccardo Poli",
  title =        "Evolution of Recursive Transistion Networks for
                 Natural Language Recognition with Parallel Distributed
                 Genetic Programming",
  booktitle =    "Evolutionary Computing",
  year =         "1997",
  editor =       "David Corne and Jonathan L. Shapiro",
  volume =       "1305",
  series =       "Lecture Notes in Computer Science",
  pages =        "163--177",
  address =      "Manchester, UK",
  month =        "11-13 " # apr,
  organisation = "AISB",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, PDGP",
  ISBN =         "3-540-63476-2",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-63476-2",
  URL =          "http://cswww.essex.ac.uk/staff/rpoli/papers/Poli-AISB-1997.pdf",
  URL =          "http://citeseer.ist.psu.edu/355686.html",
  DOI =          "doi:10.1007/BFb0027173",
  size =         "15 pages",
  abstract =     "This paper describes an application of Parallel
                 Distributed Genetic Programming (PDGP) to the problem
                 of inducing recognisers for natural language from
                 positive and negative examples. PDGP is a new 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.
                 PDGP allows the exploration of a large space of
                 possible programs including standard tree-like
                 programs, logic networks, neural networks, finite state
                 automata, Recursive Transition Networks (RTNs), etc.
                 The paper describes the representations, the operators
                 and the interpreters used in PDGP, and describes how
                 these can be tailored to evolve RTN-based
                 recognisers.",
  notes =        "see also \cite{poli:1996:RTNtr}

                 Proceedings of the Workshop on Artificial Intelligence
                 and Simulation of Behaviour (AISB) International
                 Workshop on Evolutionary Computing. Workshop in
                 Manchester, UK, April 7-8, 1997 AISB-97

                 Here PDGP was used to evolve recursive transition
                 networks used to recognise whether natural language
                 sentences are grammatical. No comparison with GP was
                 possibile.",
}

Genetic Programming entries for Riccardo Poli

Citations