Using Genetic Programming to Estimate Performance of Computational Intelligence Models

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

@InProceedings{conf/icannga/SmidN13,
  author =       "Jakub Smid and Roman Neruda",
  title =        "Using Genetic Programming to Estimate Performance of
                 Computational Intelligence Models",
  booktitle =    "Proceedings 11th International Conference on Adaptive
                 and Natural Computing Algorithms, ICANNGA 2013",
  year =         "2013",
  editor =       "Marco Tomassini and Alberto Antonioni and 
                 Fabio Daolio and Pierre Buesser",
  volume =       "7824",
  series =       "Lecture Notes in Computer Science",
  pages =        "169--178",
  address =      "Lausanne, Switzerland",
  month =        apr # " 4-6",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  bibdate =      "2013-05-27",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/icannga/icannga2013.html#SmidN13",
  isbn13 =       "978-3-642-37212-4",
  URL =          "http://dx.doi.org/10.1007/978-3-642-37213-1",
  DOI =          "doi:10.1007/978-3-642-37213-1_18",
  size =         "10 pages",
  abstract =     "This paper deals with the problem of choosing the most
                 suitable model for a new data mining task. The metric
                 is proposed on the data mining tasks space, and similar
                 tasks are identified based on this metric. A function
                 estimating models performance on the new task from both
                 the time and error point of view is evolved by means of
                 genetic programming. The approach is verified on data
                 containing results of several hundred thousands machine
                 learning experiments.",
}

Genetic Programming entries for Jakub Smid Roman Neruda

Citations