The Application Of Genetic Programming In Data Classification

Data classification is through the analysis of the data to identify the relationship between them, giving the data to some sort of significance and relevance. Nowadays,Data classification has already became a hot topic in the data mining . It has been widely used in the data mining and artificial intelligence area. People obtain the useful information which implicit in data through the data classification. With the development of society, the data we need to process is getting bigger and bigger.。manual methods already could not satisfy people's request about the real-time and accurate of information.Then people began to use computers to carry out data classification. So algorithm for data classification has become hotspot. This article applied genetic programming algorithm to the field of data classification and has made some improvements to the algorithm based on the characteristics of data classification problem.The basic idea of genetic programming come from the natural process of biological evolution and evolutionary approach, through the evolution operations, to obtain the optimal solution to the problem. This paper describes the theory of genetic programming and basic knowledge of evolutionary computation; Analysis of the characteristics of genetic programming; Studied the use of genetic programming to solve the data classification problem; And aimed at the technical limitation of genetic programming for data classification problem ,we have make some analysis improvement .This article will apply Genetic programming for data classification and do some work around the shortening evolution time of genetic programming classifier . First,through to research and analysis the basic principles of genetic programming to discover the main factors influence evolution time.①population size;②introns. through statistical experiments we obtained how the main control parameters of genetic programming influence introns. And determine the reasonable range of parameters.Then, aimed at the problem of the expansion of population and introns, even influence evolution time of genetic programming classifier. Put forward the solution which add the size controlling factor to fitness function . through experiments obtained the changes in the size of the individual before and after adding the size controlling factor . The results show that, the size of the individual with in a population has been effectively controlled after the size controlling factor added ,at the same time classifier accuracy rate has not declined. This is of great help to shorten the evolution time of the classifier and improve the classification efficiency. Finally, aimed at the slowly convergence in the early evolution of Genetic Programming system , put forward the method that adjust the rate of mutation and reproduetion operations according to the range of average fitness value dynamicly at the early evolution of the classifier. At the early evolution,in the case of fewer genes in the population, Increase the rate of mutation and reduced the rate of reproduetion advisably to increase the chances of Good genes to appear, reduce the bad genes, and introns transmit to the next generation by reproduetion operation . Experimental results show that,the method that control parameters for the classifiers at early evolution give much help to speed up the convergence rate.