A survey on the application of genetic programming to classification

Abstract—Classification is one of the most researched questions in machine learning and data mining. A wide range of real prob-lems have been stated as classification problems, for example credit scoring, bankruptcy prediction, medical diagnosis, pattern recog-nition, 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 classi-fiers. 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. Index Terms—Classification, decision trees, ensemble classifiers, feature construction, feature selection, genetic programming (GP), rule-based systems. I