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

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@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