An Inductive Programming Approach to Algebraic Specification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Hamel:2007:AAIP,
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author = "Lutz Hamel and Chi Shen",
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title = "An Inductive Programming Approach to Algebraic
Specification",
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booktitle = "Proceedings of the ECML 2007 Workshop on Approaches
and Applications of Inductive Programming (AAIP'07)",
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year = "2007",
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pages = "3--15",
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address = "Warsaw",
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month = "17-21 " # sep,
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keywords = "genetic algorithms, genetic programming",
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URL = "http://homepage.cs.uri.edu/faculty/hamel/pubs/aaip07-hamel.pdf",
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URL = "http://www.ecmlpkdd2007.org/CD/workshops/AAIP/hamel_shen/hamel_shen.pdf",
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size = "12 pages",
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abstract = "Inductive machine learning suggests an alternative
approach to the algebraic specification of software
systems: rather than using test cases to validate an
existing specification we use the test cases to induce
a specification. In the algebraic setting test cases
are ground equations that represent specific aspects of
the desired system behavior or, in the case of negative
test cases, represent specific behavior that is to be
excluded from the system. We call this inductive
equational logic programming. We have developed an
algebraic semantics for inductive equational logic
programming where hypotheses are cones over
specification diagrams. The induction of a hypothesis
or specification can then be viewed as a search problem
in the category of cones over a specific specification
diagram for a cone that satisfies some pragmatic
criteria such as being as general as possible. We have
implemented such an induction system in the functional
part of the Maude specification language using
evolutionary computation as a search strategy.",
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notes = "Department of Computer Science and Statistics
University of Rhode Island Kingston, RI 02881, USA",
- }
Genetic Programming entries for
Lutz Hamel
Chi Shen
Citations