Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Digit Recognition


This paper discusses the use of genetic algorithtm for feature selection for handwriting recognition. Its novelty lies in the use of a multi-objective genetic algorithms where sensitivity analysis and neural network are employed to allow the use of a representative database to evaluate fitness and the use of a validation database to identify the subsets of selected features that provide a good generalization. Comprehensive experiments on the NIST database confirm the effectiveness of the proposed strategy.