The effects of fitness functions on genetic programming-based ranking discovery for web search

Created by W.Langdon from gp-bibliography.bib Revision:1.8110

@Article{Fan2004jasist,

• author = "Weiguo Fan and Edward A. Fox and Praveen Pathak and Harris Wu",
• title = "The effects of fitness functions on genetic programming-based ranking discovery for web search",
• journal = "Journal of the American Society for Information Science and Technology",
• year = "2004",
• volume = "55",
• number = "7",
• pages = "628--636",
• keywords = "genetic algorithms, genetic programming, ranking function, text mining, web search, information retrieval",
• URL = "http://filebox.vt.edu/users/wfan/paper/ARRANGER/JASIST2004.pdf",
• DOI = "doi:10.1002/asi.20009",
• abstract = "Genetic-based evolutionary learning algorithms, such as genetic algorithms (GAs) and genetic programming (GP), have been applied to information retrieval (IR) since the 1980s. Recently, GP has been applied to a new IR task- discovery of ranking functions for Web search-and has achieved very promising results. However, in our prior research, only one fitness function has been used for GP-based learning. It is unclear how other fitness functions may affect ranking function discovery for Web search, especially since it is well known that choosing a proper fitness function is very important for the effectiveness and efficiency of evolutionary algorithms. In this article, we report our experience in contrasting different fitness function designs on GP-based learning using a very large Web corpus. Our results indicate that the design of fitness functions is instrumental in performance improvement. We also give recommendations on the design of fitness functions for genetic-based information retrieval experiments.",

}

Genetic Programming entries for Weiguo Fan Edward A Fox Praveen Pathak Harris Wu

Citations