Journal
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Volume 5, Issue 1, Pages 17-26Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/4235.910462
Keywords
data mining; evolutionary computation; genetic programming; neural networks
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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 occuring 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 comparable in classification and generalization.
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