A comparison of linear genetic programming and neural networks inmedical data mining

  • Authors:
  • M. Brameier;W. Banzhaf

  • Affiliations:
  • Fachbereich Inf., Dortmund Univ.;-

  • Venue:
  • IEEE Transactions on Evolutionary Computation
  • Year:
  • 2001

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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