Genetic Programming With Meta-Search: Searching For a Successful Population Within The Classification Domain

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

@InProceedings{loveard03,
  author =       "Thomas Loveard",
  title =        "Genetic Programming With Meta-Search: Searching For a
                 Successful Population Within The Classification
                 Domain",
  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 =        "119--129",
  address =      "Essex",
  publisher_address = "Berlin",
  month =        "14-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-00971-X",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.534.3317",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.534.3317",
  URL =          "http://goanna.cs.rmit.edu.au/~vc/papers/eurogp-03.pdf",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=119",
  DOI =          "doi:10.1007/3-540-36599-0_11",
  abstract =     "The genetic programming (GP) search method can often
                 vary greatly in the quality of solution derived from
                 one run to the next. As a result, it is often the case
                 that a number of runs must be performed to ensure that
                 an effective solution is found. This paper introduces
                 several methods which attempt to better use the
                 computational resources spent on performing a number of
                 independent GP runs. Termed meta-search strategies,
                 these methods seek to search the space of evolving GP
                 populations in an attempt to focus computational
                 resources on those populations which are most likely to
                 yield competitive solutions. Two meta-search strategies
                 are introduced and evaluated over a set of
                 classification problems. The meta-search strategies are
                 termed a pyramid search strategy and a population beam
                 search strategy. Additional to these methods, a
                 combined approach using properties of both the pyramid
                 and population beam search methods is evaluated.

                 Over a set of five classification problems, results
                 show that meta-search strategies can substantially
                 improve the accuracy of solutions over those derived by
                 a set of independent GP runs. In particular the
                 combined approach is demonstrated to give more accurate
                 classification performance whilst requiring less time
                 to train than a set of independent GP runs, making this
                 method a promising approach for problems for which
                 multiple GP runs must be performed to ensure a quality
                 solution.",
  notes =        "EuroGP'2003 held in conjunction with EvoWorkshops
                 2003",
}

Genetic Programming entries for Thomas Loveard

Citations