Learning Concept Descriptions with Typed Evolutionary Programming

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@Article{10.1109/TKDE.2005.199,

• author = "Claire J. Thie and Christophe Giraud-Carrier",
• title = "Learning Concept Descriptions with Typed Evolutionary Programming",
• journal = "IEEE Transactions on Knowledge and Data Engineering",
• volume = "17",
• number = "12",
• year = "2005",
• ISSN = "1041-4347",
• pages = "1664--1677",
• publisher = "IEEE Computer Society",
• address = "Los Alamitos, CA, USA",
• keywords = "genetic algorithms, genetic programming, STGP, Concept learning, typed evolutionary programming",
• DOI = "doi:10.1109/TKDE.2005.199",
• 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.",
• notes = "Claire Julia Kennedy",

}

Genetic Programming entries for Claire J Kennedy Christophe Giraud-Carrier

Citations