Learning Concept Descriptions with Typed Evolutionary Programming
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- @Article{10.1109/TKDE.2005.199,
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author = "Claire J. Thie and Christophe Giraud-Carrier",
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title = "Learning Concept Descriptions with Typed Evolutionary
Programming",
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journal = "IEEE Transactions on Knowledge and Data Engineering",
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volume = "17",
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number = "12",
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year = "2005",
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ISSN = "1041-4347",
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pages = "1664--1677",
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publisher = "IEEE Computer Society",
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address = "Los Alamitos, CA, USA",
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keywords = "genetic algorithms, genetic programming, STGP, Concept
learning, typed evolutionary programming",
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DOI = "doi:10.1109/TKDE.2005.199",
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abstract = "Examples and concepts in traditional concept learning
tasks are represented with the attribute-value
language. While enabling efficient implementations, we
argue that such propositional representation is
inadequate when data is rich in structure. This paper
describes STEPS, a strongly-typed evolutionary
programming system designed to induce concepts from
structured data. STEPS' higher-order logic
representation language enhances expressiveness, while
the use of evolutionary computation dampens the effects
of the corresponding explosion of the search space.
Results on the PTE2 challenge, a major real-world
knowledge discovery application from the molecular
biology domain, demonstrate promise.",
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notes = "Claire Julia Kennedy",
- }
Genetic Programming entries for
Claire J Kennedy
Christophe Giraud-Carrier
Citations