Genetic algorithms for linear feature extraction

In this work, a new technique for linear feature extraction and data projection using genetic algorithms (GA) is presented. GAs are employed to find linear projections in order to reduce the original number of features or to provide meaningful representations of the original data. The proposed technique is compared with well-known methods such as principal component analysis (PCA) and neural networks for non-linear discriminant analysis (NDA). A comparative study of these methods with several data sets is presented.