A comparison of genetic programming representations for binary data classification

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

@InProceedings{Dufourq:2013:WICTa,
  author =       "Emmanuel Dufourq and Nelishia Pillay",
  booktitle =    "2013 Third World Congress on Information and
                 Communication Technologies (WICT)",
  title =        "A comparison of genetic programming representations
                 for binary data classification",
  year =         "2013",
  pages =        "134--140",
  abstract =     "The choice of which representation to use when
                 applying genetic programming (GP) to a problem is
                 vital. Certain representations perform better than
                 others and thus they should be selected wisely. This
                 paper compares the three most commonly used GP
                 representations for binary data classification
                 problems, namely arithmetic trees, logical trees, and
                 decision trees. Several different function sets were
                 tested to determine which functions are more useful.
                 The different representations were tested on eight data
                 sets with different characteristics and the findings
                 show that all three representations perform similarly
                 in terms of classification accuracy. Decision trees
                 obtained the highest training accuracy and logical
                 trees obtained the highest test accuracy. In the
                 context of GP and binary data classification the
                 findings of this study show that any of the three
                 representations can be used and a similar performance
                 will be achieved. For certain data sets the arithmetic
                 trees performed the best whereas the logical trees did
                 not, and for the remaining data sets the logical tree
                 performed best whereas the arithmetic tree did not.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/WICT.2013.7113124",
  month =        dec,
  notes =        "Also known as \cite{7113124}",
}

Genetic Programming entries for Emmanuel Dufourq Nelishia Pillay

Citations