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

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2 Author(s)
M. Brameier ; Fachbereich Inf., Dortmund Univ., Germany ; W. Banzhaf

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

Published in:

IEEE Transactions on Evolutionary Computation  (Volume:5 ,  Issue: 1 )