Adapting the Fitness Function in GP for Data Mining

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

@InProceedings{eggermont:1999:affGPdm,
  author =       "J. Eggermont and A. E. Eiben and J. I. {van Hemert}",
  title =        "Adapting the Fitness Function in {GP} for Data
                 Mining",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and 
                 William B. Langdon and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "193--202",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, data mining:
                 Poster",
  ISBN =         "3-540-65899-8",
  URL =          "http://www.liacs.nl/~jeggermo/publications/eurogp99.ps.gz",
  URL =          "http://www.vanhemert.co.uk/publications/eurogp99.Adapting_the_fitness_function_in_GP_for_data_mining.ps.gz",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=193",
  DOI =          "doi:10.1007/3-540-48885-5_16",
  abstract =     "We describe how the Stepwise Adaptation of Weights
                 (SAW) technique can be applied in genetic programming.
                 The SAW-ing mechanism has been originally developed for
                 and successfully used in constraint satisfaction
                 problems. Here we identify the very basic underlying
                 ideas behind SAW-ing and point out how it can be used
                 for different types of problems. In particular, SAW-ing
                 is well suited for data mining task s where the fitness
                 of a candidate solution is composed by `local scores'
                 on data records. We evaluate the power of the SAW-ing
                 mechanism on a number of benchmark classification data
                 sets. The results indicate that extending the GP with
                 the SAW-ing feature increases its performance when
                 different types of misclassifications are not weighted
                 differently, but leads to worse results when they
                 are.",
  notes =        "EuroGP'99, part of \cite{poli:1999:GP}",
}

Genetic Programming entries for Jeroen Eggermont Gusz Eiben Jano I van Hemert

Citations