Genetic Algorithm for Feature Selection with Mutual Information

A feature selection approach combining genetic algorithm (GA) with mutual information (FSGM) is proposed. In fact, FSGM is a genetic algorithm applied to feature selection. For feature selection task, an individual of GA represents a feature subset, and the fitness function is the evaluation of the feature subset. With elaborating design, the global searching and completely evaluation can be realized in FSGM. The experimental results confirm the effectiveness of the proposed algorithm in improving the generalization and reducing the over fitting of selected feature subset.