A Comparison of Linear Genetic Programming and Neural Networks in Medical Data Mining (2000)
| Venue: | IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION |
| Citations: | 85 - 12 self |
BibTeX
@ARTICLE{Brameier00acomparison,
author = {Markus Brameier and Wolfgang Banzhaf},
title = {A Comparison of Linear Genetic Programming and Neural Networks in Medical Data Mining},
journal = {IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION},
year = {2000},
volume = {5},
pages = {17--26}
}
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Abstract
We apply linear genetic programming to several diagnosis problems in medicine. An efficient algorithm is presented that eliminates intron code in linear genetic programs. This results in a significant speedup which is especially interesting when operating with complex datasets as they are occuring in real-world applications like medicine. We compare our results to those obtained with neural networks and argue that genetic programming is able to show similar performance in classification and generalization even within a relatively small number of generations.
