Experimental Study of Multipopulation Parallel Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "F. Fernandez and M. Tomassini and 
                 W. F. {Punch III} and J. M. Sanchez",
  title =        "Experimental Study of Multipopulation Parallel Genetic
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and 
                 William B. Langdon and Julian F. Miller and Peter Nordin and 
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "283--293",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming: Poster",
  ISBN =         "3-540-67339-3",
  URL =          "http://garage.cse.msu.edu/papers/GARAGe00-03-01.pdf",
  URL =          "http://citeseer.ist.psu.edu/445504.html",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=283",
  DOI =          "doi:10.1007/978-3-540-46239-2_21",
  size =         "11 pages",
  abstract =     "The parallel execution of several populations in
                 evolutionary algorithms has usually given good results.
                 Nevertheless, researchers have to date drawn
                 conflicting conclusions when using some of the parallel
                 genetic programming models. One aspect of the conflict
                 is population size, since published GP works do not
                 agree about whether to use large or small populations.
                 This paper presents an experimental study of a number
                 of common GP test problems. Via our experiments, we
                 discovered that an optimal range of values exists. This
                 assists us in our choice of population size and in the
                 selection of an appropriate parallel genetic
                 programming model. Finding efficient parameters helps
                 us to speed up our search for solutions. At the same
                 time, it allows us to locate features that are common
                 to parallel genetic programming and the classic genetic
                 programming technique.",
  notes =        "EuroGP'2000, part of \cite{poli:2000:GP}",

Genetic Programming entries for Francisco Fernandez de Vega Marco Tomassini William F Punch Juan Manuel Sanchez Perez