Genetic Program Feature Selection for Epistatic Problems using a GA+ANN Hybrid Approach

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

@Misc{oai:CiteSeerX.psu:10.1.1.460.1644,
  title =        "Genetic Program Feature Selection for Epistatic
                 Problems using a {GA+ANN} Hybrid Approach",
  author =       "Jesse Craig and Colin Rickert and Ian Kavanagh and 
                 Jane {Brooks Zurn}",
  year =         "2006?",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.460.1644",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  keywords =     "genetic algorithms, genetic programming, artificial
                 intelligence, automatic programming, program synthesis,
                 artificial neural networks, classification, feature
                 selection, epistatic problems, problem",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.460.1644",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.460.1644.pdf",
  broken =       "http://pdf.aminer.org/000/225/956/improving_gp_classifier_generalization_using_a_cluster_separation_metric.pdf",
  size =         "8 pages",
  abstract =     "We implemented a method to improve the accuracy of a
                 genetic program (GP) for classifying an epistatic data
                 population by limiting the number of population
                 features passed to the GP. An epistatic population was
                 generated and used, where the correct combination of
                 true features was necessary in order to correctly
                 classify each member of the population. Our method of
                 limiting the number of features passed to the GP used a
                 genetic algorithm (GA) with an artificial neural
                 network (ANN) serving as the GA{'}s fitness function.
                 Limiting the number of features sent to the GP with the
                 GA+ANN method resulted in significantly better fitness
                 (Student{'}s paired samples t-test, p < 0.000) than use
                 of the entire feature set with the GP. The GA+ANN
                 method also performed significantly better in the
                 presence of noise, with better output fitness for p =
                 0.000 for 2.5percent mis-classified training instances
                 in the population and p = 0.005 for 5.0percent
                 mis-classified population training instances.",
}

Genetic Programming entries for Jesse Craig Colin Rickert Ian Kavanagh Jane Brooks Zurn

Citations