Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

Improve the Adaptive Capability of TMA-OR

  • Conference paper
Distributed Computing and Artificial Intelligence

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 151))

Abstract

In T-detector Maturation Algorithm with Overlap Rate (TMA-OR), the parameters Omin and self radius rs are required to be set by experience. To solve the problem, negative selection operator and self radius learning mechanism are proposed. The results of experiment show that the proposed algorithm can achieve the same effect when KDD and iris are as data set.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 429.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 549.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Hart, E., Timmis, J.: Application areas of AIS: The past, the present and the future. Journal of Applied Soft Computing 8(1), 191–201 (2008)

    Article  Google Scholar 

  2. Timmis, J., et al.: An interdisciplinary perspective on artificial immune systems. Evolutionary Intelligence 1(1), 5–26 (2008)

    Article  Google Scholar 

  3. Greensmith, J., Aickelin, U., et al.: Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm. Information Fusion 11(1), 21–34 (2010)

    Article  Google Scholar 

  4. Ji, Z., Dasgupta, D.: Real-Valued Negative Selection Algorithm with Variable-Sized Detectors. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 287–298. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  5. Stibor, T., Timmis, J.I., Eckert, C.: A Comparative Study of Real-Valued Negative Selection to Statistical Anomaly Detection Techniques. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 262–275. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  6. Ji, Z., Dasgupta, D.: Estimating the Detector Coverage in a Negative Selection Algorithm. In: Genetic and Evolutionary Computation Conference (2005)

    Google Scholar 

  7. Chen, J.: T-detector Maturation Algorithm with Overlap Rate. WSEAS Transactions on Computers 7(8), 1300–1308 (2008)

    Google Scholar 

  8. Chen, J.: A novel suppression operator used in optaiNet. BSBT 57, 17–23 (2009)

    Google Scholar 

  9. Ji, Z., Dasgupta, D.: Augmented Negative Selection Algorithm with Variable-Coverage Detectors. In: Congress on Evolutionary Computation, vol. 1, pp. 1081–1088 (2004)

    Google Scholar 

  10. Ji, Z.: Negative Selection Algorithms: from the Thymus to V-detector. PhD Dissertation, University of Memphis (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electronic Information Department, Zhejiang Wanli University, No.8 South Qian Hu Road, Ningbo, Zhejiang, 315100, China

    Jungan Chen, Qiaowen Zhang & Zhaoxi Fang

Authors
  1. Jungan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qiaowen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhaoxi Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jungan Chen .

Editor information

Editors and Affiliations

  1. Dept. Computer Science &, Intelligent Systems, Osaka Prefecture University, Gakuen-chu 1-1, Sakai, Osaka, 599-8531, Japan

    Sigeru Omatu

  2. , Department of Computing Science, University of Salamanca, Plaza de la Merced S/N, Salamanca, 37008, Spain

    Juan F. De Paz Santana

  3. , Department of Computing Science, University of Salamanca, Plaza de la Merced S/N, Salamanca, 37008, Spain

    Sara Rodríguez González

  4. Escuela Politécnica Superior (EPS), Depto. Informática, Universidad Carlos III de Madrid, Avenida de la Universidad Carloss III 22, Madrid, 28270, Spain

    Jose M. Molina

  5. , Data Processing and Simulation Group, Universidad Politécnica de Madrid, Calle de Ramiro de Maeztu, 7, Madrid, 28040, Spain

    Ana M. Bernardos

  6. Faculty of Science, Department of Computing Science, University of Salamanca, Plaza de la Merced S/N, Salamanca, 37008, Spain

    Juan M. Corchado Rodríguez

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chen, J., Zhang, Q., Fang, Z. (2012). Improve the Adaptive Capability of TMA-OR. In: Omatu, S., De Paz Santana, J., González, S., Molina, J., Bernardos, A., Rodríguez, J. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28765-7_80

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28765-7_80

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28764-0

  • Online ISBN: 978-3-642-28765-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation