Journal Article10.1109/4235.910462
A comparison of linear genetic programming and neural networks in medical data mining
Markus Brameier,Wolfgang Banzhaf +1 more
TL;DR: An efficient algorithm that eliminates intron code and a demetic approach to virtually parallelize the system on a single processor are discussed, which show that GP performs comparably in classification and generalization.
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Abstract: We introduce a new form of linear genetic programming (GP). Two methods of acceleration of our GP approach are discussed: 1) an efficient algorithm that eliminates intron code and 2) a demetic approach to virtually parallelize the system on a single processor. Acceleration of runtime is especially important when operating with complex data sets, because they are occurring in real-world applications. We compare GP performance on medical classification problems from a benchmark database with results obtained by neural networks. Our results show that GP performs comparably in classification and generalization.
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