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A Fuzzy Embedded GA for Information Retrieving from Related Data Set

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MICAI 2006: Advances in Artificial Intelligence (MICAI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4293))

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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.

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Author information

Authors and Affiliations

  1. Computer Science Department, ZhongShan University, GuangZhou, 510275, China

    Yang Yi, JinFeng Mei & ZhiJiao Xiao

Authors
  1. Yang Yi
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  2. JinFeng Mei
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  3. ZhiJiao Xiao
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Editor information

Editors and Affiliations

  1. Center for Computing Research, National Polytechnic Institute, 07738, Mexico City, México

    Alexander Gelbukh

  2. Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Luis Enrique Erro No. 1, Sta. Ma. Tonanzintla, 72840, Puebla, México

    Carlos Alberto Reyes-Garcia

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© 2006 Springer-Verlag Berlin Heidelberg

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Yi, Y., Mei, J., Xiao, Z. (2006). A Fuzzy Embedded GA for Information Retrieving from Related Data Set. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_90

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  • DOI: https://doi.org/10.1007/11925231_90

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-49026-5

  • Online ISBN: 978-3-540-49058-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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