A Survey on the Application of Genetic Programming to Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@Article{Espejo:2010:ieeetSMC,

• author = "Pedro G. Espejo and Sebastian Ventura and Francisco Herrera",
• title = "A Survey on the Application of Genetic Programming to Classification",
• journal = "IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews",
• year = "2010",
• month = mar,
• volume = "40",
• number = "2",
• pages = "121--144",
• keywords = "genetic algorithms, genetic programming, Classification, decision trees, ensemble classifiers, feature construction, feature selection, rule-based systems",
• abstract = "Classification is one of the most researched questions in machine learning and data mining. A wide range of real problems have been stated as classification problems, for example credit scoring, bankruptcy prediction, medical diagnosis, pattern recognition, text categorization, software quality assessment, and many more. The use of evolutionary algorithms for training classifiers has been studied in the past few decades. Genetic programming (GP) is a flexible and powerful evolutionary technique with some features that can be very valuable and suitable for the evolution of classifiers. This paper surveys existing literature about the application of genetic programming to classification, to show the different ways in which this evolutionary algorithm can help in the construction of accurate and reliable classifiers.",
• DOI = "doi:10.1109/TSMCC.2009.2033566",
• ISSN = "1094-6977",
• notes = "Also known as \cite{5340522}",

}

Genetic Programming entries for Pedro Gonzalez Espejo Sebastian Ventura Francisco Herrera

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