A comparison of linear genetic programming and neural networks in medical data mining

We introduce a new form of linear genetic programming (GP). Two methods of acceleration of our GP approach are discussed: 1) an efficient algorithm th at eliminates intron code and 2) a demetic approach to virtually paralleliz e the system on a single processor. Acceleration of runtime is especially i mportant when operating with complex data sets, because they are occuring i n real-world applications. We compare GP performance on medical classificat ion problems from a benchmark database with results obtained by neural netw orks. Our results show that GP performs comparable in classification and ge neralization.