Genetic algorithms for feature selection and weighting, a review and study

Our aim is: a) To present a comprehensive sur,ey of previous attempts at using Genetic Algorithms (GA) for feature selection in Pattern Recognition (PR) applications, with a special focus on Character Recognition: and b) To report on new work that uses GA to optimize the weights of the classification module of a character recognition system (CRS). The main purpose of feature selection is to reduce the number of features, by eliminating irrelevant and redundant features, while simultaneously maintaining or enhancing classification accuracy. Mane search algorithms have been used for feature selection. Among those, GA have proven to be an effective computational method, especially in situations where the search space is uncharacterized (mathematically), not fully understood, or/and highly dimensional.