The contruction and evaluation of decision trees: A comparison of evolutionary and concept learning methods
The CALTROP program which is presented in this paper provides a test of the feasibility of representing a decision tree as a linear chromosome and applying a genetic algorithm to the optimisation of the decision tree with respect to the classification of test sets of example data. The unit of the genetic alphabet (the “caltrop”) is a 3-integer string corresponding to a subtree of the decision tree. The program offers a user a choice of mating strategies and mutation rates. Test runs with different data sets show that the decision trees produced by the CALTROP program usually compare favourably with those produced by the popular automatic induction algorithm, ID3.
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