The effects of fitness functions on genetic programming-based ranking discovery for web search
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Fan2004jasist,
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author = "Weiguo Fan and Edward A. Fox and Praveen Pathak and
Harris Wu",
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title = "The effects of fitness functions on genetic
programming-based ranking discovery for web search",
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journal = "Journal of the American Society for Information
Science and Technology",
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year = "2004",
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volume = "55",
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number = "7",
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pages = "628--636",
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keywords = "genetic algorithms, genetic programming, ranking
function, text mining, web search, information
retrieval",
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URL = "http://filebox.vt.edu/users/wfan/paper/ARRANGER/JASIST2004.pdf",
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DOI = "doi:10.1002/asi.20009",
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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