Document (#27240)

Author

Fan, W.
Fox, E.A.
Pathak, P.
Wu, H.

Title

¬The effects of fitness functions an genetic programming-based ranking discovery for Web search

Source

Journal of the American Society for Information Science and technology. 55(2004) no.7, S.628-636

Year

2004

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 taskdiscovery 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 weIl 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 an 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 an the design of fitness functions for genetic-based information retrieval experiments.

Theme

Retrievalalgorithmen

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5. Qin, T.; Zhang, X.-D.; Tsai, M.-F.; Wang, D.-S.; Liu, T.-Y.; Li, H.: Query-level loss functions for information retrieval (2008) 0.36

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