This paper experimentally compares three approaches to program induction: inductive logic programming (ILP), genetic programming (GP), and genetic logic programming (GLP) (a variant of GP for inducing Prolog programs). Each of these methods was used to induce four simple, recursive, list-manipulation functions. The results indicate that ILP is the most likely to induce a correct program from small sets of random examples, while GP is generally less accurate. GLP performs the worst, and is rarely able to induce a correct program. Interpretations of these results in terms of differences in search methods and inductive biases are presented.

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Technical Report AI 98-271, Artificial Intelligence Lab, University of Texas at Austin.

@techreport{tang:tech98, title={An Experimental Comparison of Genetic Programming and Inductive Logic Programming on Learning Recursive List Functions}, author={Lappoon R. Tang and Mary Elaine Califf and Raymond J. Mooney}, number={AI 98-271}, month={May}, institution={Artificial Intelligence Lab, University of Texas at Austin}, url="http://www.cs.utexas.edu/users/ai-lab?tang:tech98", year={1998} }