Using Genetic Programming to Evolve Weighting Schemes for the Vector Space Model of Information Retrieval

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@InProceedings{cummins:2004:lbp,

• author = "Ronan Cummins and Colm O'Riordan",
• title = "Using Genetic Programming to Evolve Weighting Schemes for the Vector Space Model of Information Retrieval",
• booktitle = "Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference",
• year = "2004",
• editor = "Maarten Keijzer",
• address = "Seattle, Washington, USA",
• month = "26 " # jul,
• keywords = "genetic algorithms, genetic programming",
• URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP038.pdf",
• abstract = "Term weighting in many Information Retrieval models is of crucial importance in the research and development of accurate retrieval systems. This paper explores a method to automatically determine suitable term weighting schemes for the vector space model. Genetic Programming is used to automatically evolve weighting schemes that return a high average precision. These weighting functions are tested on well-known test collections and compared to the tf-idf based weighting scheme using standard Information Retrieval performance metrics.",
• notes = "Part of \cite{keijzer:2004:GECCO:lbp}",

}

Genetic Programming entries for Ronan Cummins Colm O'Riordan

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