A Comparison of Genetic Programming and Neural Networks in Medical Data Analysis

We apply an interpreting variant of linear genetic programming to several diagnosis problems in medicine. We compare our results to results obtained with neural networks and argue that genetic programming is able to show similar performances in classi cation and generalization even when using a relatively small number of generations. Finally, an e cient algorithm for the elimination of introns in linear genetic programs is presented.