Evolving Finite State Transducers: Some Initial Explorations

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

  author =       "Simon M. Lucas",
  title =        "Evolving Finite State Transducers: Some Initial
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2003",
  year =         "2003",
  editor =       "Conor Ryan and Terence Soule and Maarten Keijzer and 
                 Edward Tsang and Riccardo Poli and Ernesto Costa",
  volume =       "2610",
  series =       "LNCS",
  pages =        "130--141",
  address =      "Essex",
  publisher_address = "Berlin",
  month =        "14-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-00971-X",
  URL =          "http://algoval.essex.ac.uk/rep/fst/EuroFST.pdf",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=130",
  DOI =          "doi:10.1007/3-540-36599-0_12",
  abstract =     "Finite state transducers (FSTs) are finite state
                 machines that map strings in a source domain into
                 strings in a target domain. While there are many
                 reports in the literature of evolving general finite
                 state machines, there has been much less work on
                 evolving FSTs. In particular, the fitness functions
                 required for evolving FSTs are generally different to
                 those used for FSMs. This paper considers three
                 string-distance based fitness functions. We compute
                 their fitness distance correlations, and present
                 results on using two of these (Strict and Hamming) to
                 evolve FSTs. We can control the difficulty of the
                 problem by the presence of short strings in the
                 training set, which make the learning problem easier.
                 In the case of the harder problem, the Hamming measure
                 performs best, while the Strict measure performs best
                 on the easier problem.",
  notes =        "EuroGP'2003 held in conjunction with EvoWorkshops

Genetic Programming entries for Simon M Lucas