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Integrating Genetic Algorithms and Fuzzy Logic for Web Structure Optimization

  • Iltae Lee
  • Negar Koochakzadeh
  • Keivan Kianmehr
  • Reda Alhajj
  • Jon Rokne
Chapter
Part of the Annals of Information Systems book series (AOIS, volume 12)

Abstract

This chapter addresses the restructuring of Websites by an approach that integrates fuzziness weighted page rank (WPR) index and log rank index for pages of the considered Website. Fuzzy logic gives a degree of a membership to a problem and, hence, more adequately describes reasoning to a problem than a numeric deviation value does (the difference between the WPR index and log rank index), which does not give accurate human reasoning. Using fuzzy logic, the computational program translates a deviation value to a fuzzy representation by producing statements like “page A has a low restructuring factor by degree 0.8.” However, without well-defined membership functions, a fuzzy value can be as meaningless as or even worse than a deviation value. Accordingly, we have shown how genetic algorithms (GA) can be applied to optimize the fuzzy membership functions. This chapter demonstrates how fuzzy logic can be applied to a deviation value to better represent the degree of restructuring.

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Copyright information

© Springer US 2010

Authors and Affiliations

  • Iltae Lee
    • 1
  • Negar Koochakzadeh
    • 1
  • Keivan Kianmehr
    • 1
  • Reda Alhajj
    • 1
    • 2
  • Jon Rokne
    • 1
  1. 1.Department of Computer ScienceUniversity of CalgaryCalgaryCanada
  2. 2.Department of Computer ScienceGlobal UniversityBeirutLebanon

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