Applying layered multi-population genetic programming on learning to rank for information retrieval

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Lin:2012:ICMLC,

• author = "Jung Yi Lin and Jen-Yuan Yeh and Chao-Chung Liu",
• booktitle = "International Conference on Machine Learning and Cybernetics (ICMLC 2012)",
• title = "Applying layered multi-population genetic programming on learning to rank for information retrieval",
• year = "2012",
• volume = "5",
• pages = "1754--1759",
• size = "6 pages",
• abstract = "Information retrieval (IR) returns a relative ranking of documents with respect to a user query. Learning to rank for information retrieval (LR4IR) employs supervised learning techniques to address this problem, and it aims to produce a ranking model automatically for defining a proper sequential order of related documents based on the query. The ranking model determines the relationship degree between documents and the query. In this paper an improved version of RankGP is proposed. It uses layered multi-population genetic programming to obtain a ranking function which consists of a set of IR evidences and particular predefined operators. The proposed method is capable to generate complex functions through evolving small populations. In this paper, LETOR 4.0 was used to evaluate the effectiveness of the proposed method and the results showed that the method is competitive with other LR4IR Algorithms.",
• keywords = "genetic algorithms, genetic programming, document handling, learning (artificial intelligence), query processing, LETOR 4.0, LR4IR, RankGP, document ranking, layered multipopulation genetic programming, learning to rank for information retrieval, ranking function, supervised learning techniques, user query, Abstracts, Programming, Sociology, Statistics, Evolutionary computation, Learning to rank for Information Retrieval, Ranking function",
• DOI = "doi:10.1109/ICMLC.2012.6359640",
• ISSN = "2160-133X",
• notes = "Also known as \cite{6359640}",

}

Genetic Programming entries for Mick Jung-Yi Lin Jen-Yuan Yeh Chao Chung Liu

Citations