Eric Chang, Richard P. Lippmann
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. On a 338 training pattern vowel-recognition problem with 10 classes, genetic algorithms reduced the number of stored exemplars from 338 to 43 without significantly increasing classification er(cid:173) ror rate. In all applications, genetic algorithms were easy to apply and found good solutions in many fewer trials than would be required by ex(cid:173) haustive search. Run times were long, but not unreasonable. These results suggest that genetic algorithms are becoming practical for pattern classi(cid:173) fication problems as faster serial and parallel computers are developed.