Scalability Analysis of Genetic Programming Classifiers

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

@InProceedings{Hunt:2012:CEC,
  title =        "Scalability Analysis of Genetic Programming
                 Classifiers",
  author =       "Rachel Hunt and Kourosh Neshatian and Mengjie Zhang",
  pages =        "509--516",
  booktitle =    "Proceedings of the 2012 IEEE Congress on Evolutionary
                 Computation",
  year =         "2012",
  editor =       "Xiaodong Li",
  month =        "10-15 " # jun,
  DOI =          "doi:10.1109/CEC.2012.6256520",
  address =      "Brisbane, Australia",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Complex
                 Networks and Evolutionary Computation",
  abstract =     "Genetic programming (GP) has been used extensively for
                 classification due to its flexibility, interpretability
                 and implicit feature manipulation. There are also
                 disadvantages to the use of GP for classification,
                 including computational cost, bloating and parameter
                 determination. This work analyses how GP-based
                 classifier learning scales with respect to the number
                 of examples in the classification training data set as
                 the number of examples grows, and with respect to the
                 number of features in the classification training data
                 set as the number of features grows. The scalability of
                 GP with respect to the number of examples is studied
                 analytically. The results show that GP scales very well
                 (in linear or close to linear order) with the number of
                 examples in the data set and the upper bound on testing
                 error decreases. The scalability of GP with respect to
                 the number of features is tested experimentally, with
                 results showing that the computations increase
                 exponentially with the number of features.",
  notes =        "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
                 EPS and the IET.",
}

Genetic Programming entries for Rachel Hunt Kourosh Neshatian Mengjie Zhang

Citations