Journal article
The effects of fitness functions on genetic programming-based ranking discovery for Web search
Journal of the American Society for Information Science and Technology, Vol.55(7), pp.628-636
05/2004
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.
Details
- Title: Subtitle
- The effects of fitness functions on genetic programming-based ranking discovery for Web search
- Creators
- WEIGUO Fan - Virginia TechEdward A Fox - Virginia TechPraveen Pathak - University of FloridaHarris WU - University of Michigan–Ann Arbor
- Resource Type
- Journal article
- Publication Details
- Journal of the American Society for Information Science and Technology, Vol.55(7), pp.628-636
- Publisher
- Wiley
- DOI
- 10.1002/asi.20009
- ISSN
- 1532-2882
- eISSN
- 1532-2890
- Language
- English
- Date published
- 05/2004
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380534402771
Metrics
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