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A Fuzzy Embedded GA for Information Retrieving from Related Data Set

  • Yang Yi
  • JinFeng Mei
  • ZhiJiao Xiao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)

Abstract

The arm of this work is to provide a formal model and an effective way for information retrieving from a big related data set. Based upon fuzzy logic operation, a fuzzy mathematical model of 0-1 mixture programming is addressed. Meanwhile, a density function indicating the overall possessive status of the effective mined out data is introduced. Then, a soft computing (SC) approach which is a genetic algorithm (GA) embedded fuzzy deduction is presented. During the SC process, fuzzy logic decision is taken into the uses of determining the genes’ length, calculating fitness function and choosing feasible solution. Stimulated experiments and comparison tests show that the methods can match the user’s most desired information from magnanimity data exactly and efficiently. The approaches can be extended in practical application in solving general web mining problem.

Keywords

Genetic Algorithm Fuzzy Rule Soft Computing Fuzzy Membership Fuzzy Membership Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yang Yi
    • 1
  • JinFeng Mei
    • 1
  • ZhiJiao Xiao
    • 1
  1. 1.Computer Science DepartmentZhongShan UniversityGuangZhouChina

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