A survey on the application of genetic programming to classification

Authors:
Pedro G. Espejo;Sebastián Ventura;Francisco Herrera
Affiliations:
Department of Computer Science and Numerical Analysis, University of Cordoba, Cordoba, Spain;Department of Computer Science and Numerical Analysis, University of Cordoba, Cordoba, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Year:
2010

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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.