A Comparison with Neural Networks

4.5 Summary and Conclusion

We reported on LGP applied to a number of medical classification tasks. It was demonstrated that, on average, genetic programming performs competitive to RPROP neural networks with respect to the generalization performance.

The runtime performance of genetic programming becomes especially important for time-critical applications or when operating with large data sets from real-world domains like medicine. Two techniques were presented that reduced the computational costs significantly.

First, the elimination of noneffective code from linear genetic programs resulted in an average decrease in runtime of about a factor of 5 here. Second, by using a demetic population in combination with an elitist migration strategy the number of effective generations was reduced by a factor of about 3, without decreasing the performance of the evolutionary algorithm.