An Experimental Comparison of Genetic Programming and Inductive Logic Programming on Learning Recursive List Functions
Created by W.Langdon from
gp-bibliography.bib Revision:1.6970
- @TechReport{ilpgp-ml-98,
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author = "Lappoon R. Tang and Mary Elaine Califf and
Raymond J. Mooney",
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title = "An Experimental Comparison of Genetic Programming and
Inductive Logic Programming on Learning Recursive List
Functions",
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institution = "Artificial Intelligence Lab, University of Texas at
Austin",
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year = "1998",
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number = "AI 98-271",
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address = "USA",
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month = may,
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keywords = "genetic algorithms, genetic programming",
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URL = "
http://www.cs.utexas.edu/users/ml/papers/ilpgp-ml-98.pdf",
-
URL = "
http://www.cs.utexas.edu/users/ml/papers/ilpgp-ml-98.ps.gz",
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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.",
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size = "14 pages",
- }
Genetic Programming entries for
Lappoon R Tang
Mary Elaine Califf
Raymond J Mooney
Citations