Skip to main content
Log in

Optimization of web search engine and its application to web mining

  • Published:
Wuhan University Journal of Natural Sciences

Abstract

With the explosive growth of information sources available on the World Wide Web, how to combine the results of multiple search engines has become a valuable problem. In this paper, a search strategy based on genetic simulated annealing for search engines in Web mining is proposed. According to the proposed strategy, there exists some important relationship among Web statistical studies, search engines and optimization techniques. We have proven experimentally the relevance of our approach to the presented queries by comparing the qualities of output pages with those of the original downloaded pages, as the number of iterations increases better results are obtained with reasonable execution time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Crestani F, Pasi G. Soft Computing in Information Retrieval: Techniques and Application[M]. Heidelberg: Springer-Verlag, 2000: 154–164.

    Google Scholar 

  2. Kim S, Zhang B T. Web Document Retrieval by Genetic Learning of Importance Factors for Html Tags[C]// Int’l Workshop on Text and Web Mining. New York: Springer-Verlag, 2000: 13–23.

    Google Scholar 

  3. Boughanem M, Chrisment C, Mothe J, et al. Connectionist and Genetic Approaches for Information Retrieval[C]// Soft Computing in Information Retrieval:Techniques and Applications. Heidelberg: Springer-Verlag, 2000:102–121.

    Google Scholar 

  4. Loia V, Luongo P. An Evolutionary Approach to Automatic Web Page Categorization and Updating[M]. Singapore: Springer-Verlag, 2001: 292–302.

    Google Scholar 

  5. Martino V, Mililotti M. Sub-Optimal Scheduling in a Grid Using Genetic Algorithm[J]. Parallel Computing, 2004, 30(5): 553–565.

    Article  Google Scholar 

  6. Etzioni O, Perkowitz M. Adaptive Web Sites:An AI Challenge[C]// Proc of the 15th Int’l Joint Conf Artificial Intelligence. San Francisco: Morgan Kaufmann Publishers, 1997: 16–21.

    Google Scholar 

  7. Shu Wanneng, Zheng Shijue. A Parallel Genetic Simulated Annealing Hybrid Algorithm for Task Scheduling[J]. Wuhan University Journal of Natural Sciences, 2006, 12(5): 1378–1382.

    Google Scholar 

  8. Abraham A, Buyya R. Nature’s Heuristics for Scheduling Jobs on Computational Grids[EB/OL]. [2007-12-21]. http://www.softcomputing.net/adcom.pdf .

  9. Zhang Jiangshe, Xu Zongben, Liang Yi. Global Annealing Genetic Algorithm and Its Convergence Well Necessary Condition[J]. Science in China, 1997, 27(2): 154–164.

    Google Scholar 

  10. Lu Shan, Chen Tong, Xu Shijie. Optimal Lambert Transfer Based on Adaptive Simulated Annealing Genetic Algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2007, 33(10): 1191–1195(Ch).

    Google Scholar 

  11. Wang Xia, Zhou Guobiao. Strong Convergence of Global Annealing Genetic Algorithm[J]. Mathematica Applicata, 2003, 16(3): 1–7.

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Beiji Zou.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, H., Zou, B. & Bian, N. Optimization of web search engine and its application to web mining. Wuhan Univ. J. Nat. Sci. 14, 115–118 (2009). https://doi.org/10.1007/s11859-009-0204-y

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11859-009-0204-y

Key words

CLC number

Navigation