Abstract


Application Of Genetic Programming To ClassificationClassification is one of the most researched questions in machine learning and data mining. Genetic programming is a recent development in the area of evolutionary computation. It was greatly stimulated in the 1990s by John Koza. 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. Flexibility is one of the main advantages of GP, and this feature allows GP to be applied for classification in many different ways. One of the central problems in computer science is how to make computers solve problems without being explicitly programmed to do so. Genetic programming offers a solution through the evolution of computer programs by methods of natural selection.According to Koza, genetic programming searches the space of possible computer programs that are highly fit for solving the problem at hand. This paper surveys 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 andreliable classifiers


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