Fitness Function Obtained from a Genetic Programming Approach for Web Document Clustering Using Evolutionary Algorithms

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

@InProceedings{conf/iberamia/CobosMMLH12,
  author =       "Carlos Cobos and Leydy Munoz and Martha Mendoza and 
                 Elizabeth {Leon Guzman} and Enrique Herrera-Viedma",
  title =        "Fitness Function Obtained from a Genetic Programming
                 Approach for Web Document Clustering Using Evolutionary
                 Algorithms",
  booktitle =    "Proceedings of the 13th Ibero-American Conference on
                 {AI}, {IBERAMIA} 2012",
  year =         "2012",
  editor =       "Juan Pavon and Nestor D. Duque-Mendez and 
                 Ruben Fuentes-Fernandez",
  volume =       "7637",
  series =       "Lecture Notes in Computer Science",
  pages =        "179--188",
  address =      "Cartagena de Indias, Colombia",
  month =        nov # " 13-16",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, web document
                 clustering, clustering of web results, Bayesian
                 information criteria",
  isbn13 =       "978-3-642-34653-8",
  URL =          "http://dx.doi.org/10.1007/978-3-642-34654-5",
  DOI =          "doi:10.1007/978-3-642-34654-5_19",
  bibdate =      "2012-11-17",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/iberamia/iberamia2012.html#CobosMMLH12",
  size =         "10 pages",
  abstract =     "Web document clustering (WDC) is an alternative means
                 of searching the web and has become a rewarding
                 research area. Algorithms for WDC still present some
                 problems, in particular: inconsistencies in the content
                 and description of clusters. The use of evolutionary
                 algorithms is one approach for improving results. It
                 uses standard index to evaluate the quality (as a
                 fitness function) of different solutions of clustering.
                 Indexes such as Bayesian Information Criteria (BIC),
                 Davies-Bouldin, and others show good performance, but
                 with much room for improvement. In this paper, a
                 modified BIC fitness function for WDC based on
                 evolutionary algorithms is presented. This function was
                 discovered using a genetic program (from a reverse
                 engineering view). Experiments on datasets based on
                 DMOZ show promising results.",
  notes =        "Advances in Artificial Intelligence",
}

Genetic Programming entries for Carlos Alberto Cobos Lozada Leydy Carolina Munoz Martha Eliana Mendoza Becerra Elizabeth Leon Guzman Enrique Herrera Viedma

Citations