@techreport{ilpgp-ml-98,
	title = {An Experimental Comparison of Genetic Programming and
                 Inductive Logic Programming on Learning Recursive List
                 Functions},
	address = {USA},
	author = {Lappoon R. Tang and Mary Elaine Califf and Raymond J. Mooney},
	institution = {Artificial Intelligence Lab, University of Texas at
                 Austin},
	month = {May},
	number = {AI 98-271},
	url = {http://www.cs.utexas.edu/users/ml/papers/ilpgp-ml-98.ps.gz},
	year = {1998},
	biburl = {http://www.bibsonomy.org/bibtex/26d9aedb84a1b6358ec292ffced25cda1/brazovayeye},
	abstract = {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.},
	size = {14 pages},
	keywords = {algorithms, genetic programming }
}