Using genetic algorithms to select and create features for pattern classification

Genetic algorithms were used for feature selection and creation in two pattern-classification problems. On a machine-version inspection task, it was found that genetic algorithms performed no better than conventional approaches to feature selection but required much more computation. On a difficult artificial machine-vision task, genetic algorithms were able to create new features (polynomial functions of the original features) which dramatically reduced classification error rates. Neural network and nearest-neighbor classifiers were unable to provide such low error rates using only the original features