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Improving the life cycle of Genetic Programming using Feature Selection with Multiobjective Fitness Function

In this paper, we proposed to select the optimum number of features from the datasets using genetic programming. For selecting the optimal number of features we run the GP life cycle for 50 percent of the generation and select the features present in the best classifiers and form their optimal set. After that we remove the other features in the classifier from the features present in the optimal set and run the process till the last generation. Then we select the features present in the best classifier after the last generation and called those features the optimal feature set. Keywords— feature selection, genetic programming, multiobjective fitness function.