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The effects of fitness functions on genetic programming‐based ranking discovery for Web search

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Listed:
  • Weiguo Fan
  • Edward A. Fox
  • Praveen Pathak
  • Harris Wu

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.

Suggested Citation

  • Weiguo Fan & Edward A. Fox & Praveen Pathak & Harris Wu, 2004. "The effects of fitness functions on genetic programming‐based ranking discovery for Web search," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 55(7), pages 628-636, May.
    • Handle: RePEc:bla:jamist:v:55:y:2004:i:7:p:628-636
    DOI: 10.1002/asi.20009
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    Cited by:

    1. Keyhanipour, Amir Hosein & Piroozmand, Maryam & Badie, Kambiz, 2009. "A GP-adaptive web ranking discovery framework based on combinative content and context features," Journal of Informetrics, Elsevier, vol. 3(1), pages 78-89.
    2. Hormozi, Elham & Hu, Shuwen & Ding, Zhe & Tian, Yu-Chu & Wang, You-Gan & Yu, Zu-Guo & Zhang, Weizhe, 2022. "Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation," Energy, Elsevier, vol. 252(C).

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