Genetic algorithms were used to select and create features and to select reference exemplar patterns for machine vision and speech pattern classi(cid:173) fication tasks. For a complex speech recognition task, genetic algorithms required no more computation time than traditional approaches to feature selection but reduced the number of input features required by a factor of five (from 153 to 33 features). On a difficult artificial machine-vision task, genetic algorithms were able to create new features (polynomial functions of the original features) which reduced classification error rates from 19% to almost 0%. Neural net and k nearest neighbor (KNN) classifiers were unable to provide such low error rates using only the original features. Ge(cid:173) netic algorithms were also used to reduce the number of reference exemplar patterns for a KNN classifier.

Feature selection and creation are two of the most important and difficult tasks in the field of pattern classification. Good features improve the performance of both conventional and neural network pattern classifiers. Exemplar selection is another task that can reduce the memory and computation requirements of a KNN classifier.

Feature selection and creation are two of the most important and difficult tasks in the field of pattern classification. Good features improve the performance of both conventional and neural network pattern classifiers. Exemplar selection is another task that can reduce the memory and computation requirements of a KNN classifier.

Feature selection and creation are two of the most important and difficult tasks in the field of pattern classification. Good features improve the performance of both conventional and neural network pattern classifiers. Exemplar selection is another task that can reduce the memory and computation requirements of a KNN classifier. These three tasks require a search through a space which is typically so large that 797 798 Chang and Lippmann exhaustive search is impractical. The purpose of this research was to explore the usefulness of Genetic search algorithms for these tasks. Details concerning this research are available in (Chang, 1990).