The effects of fitness functions on genetic programming-based ranking discovery for Web search: Research Articles

  • Authors:

  • Weiguo Fan;Edward A. Fox;Praveen Pathak;Harris Wu

  • Affiliations:

  • Virginia Polytechnic Institute and State University, 3007 Pamplin Hall, Blacksburg, VA 24061;Virginia Polytechnic Institute and State University, Blacksburg, VA 24061;University of Florida, Gainesville, FL 32611;University of Michigan, Ann Arbor, MI 48109

  • Venue:
  • Journal of the American Society for Information Science and Technology
  • Year:
  • 2004

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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. © 2005 Wiley Periodicals, Inc.