A Survey on the Application of Genetic Programming to Classification

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.

• Publication year

  2010

• Venue

  IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)

• Publication date

  2010-03-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1109/TSMCC.2009.2033566
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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