A Resource Limited Immune Approach for Evolving Architecture and Weights of Multilayer Neural Network

  • Xiaoyang Fu
  • Shuqing Zhang
  • Zhenping Pang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6145)


A resource limited immune approach (RLIA) was developed to evolve architecture and initial connection weights of multilayer neural networks. Then, with Back-Propagation (BP) algorithm, the appropriate connection weights can be found. The RLIA-BP classifier, which is derived from hybrid algorithm mentioned above, is demonstrated on SPOT multi-spectral image data, vowel data and Iris data effectively. The simulation results demonstrate that RLIA-BP classifier possesses better performance comparing with Bayes maximum-likelihood classifier, k-nearest neighbor classifier (k-NN), BP neural network (BP-MLP) classifier and Resource limited artificial immune classifier (AIRS) in pattern classification.


resource limited immune approach (RLIA) evolutionary artificial neural network (EANN) pattern classification 


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  1. 1.
    Tou, T.T., Gonzalez, R.C.: Pattern Recognition Principles. Addison-Wesley, New York (1974)zbMATHGoogle Scholar
  2. 2.
    Pal, S.K., Bandyopadhyay, S., Murthy, C.A.: Genetic Classifiers for Remotely Sensed Images: Comparison with Standard Methods. International Journal of Remote Sensing 2(13), 2545–2569 (2001)CrossRefGoogle Scholar
  3. 3.
    Lippmann, P.P.: Pattern Classification Using Neural networks. IEEE Communications Magazine, 47–63 (November 1989)Google Scholar
  4. 4.
    Yao, X.: Evolving artificial neural networks. Proceeding of the IEEE 87(9), 1423–1447 (1999)CrossRefGoogle Scholar
  5. 5.
    Watkins, A., Boggess, L.: A resource limited artificial immune classifier. In: Proceeding of the 2002 Congress on Evolutionary Computation (CEC 2002), Special Session on Artificial Immune System, vol. 1, pp. 926–931. IEEE Press, CA (2002)Google Scholar
  6. 6.
    Fu, X., Dale, P.E.R., Zhang, S.: Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification. Chin. Geogra. Sci. 2008 18(2), 162–170 (2008)CrossRefGoogle Scholar
  7. 7.
    Hertz, J., Krogh, A., Palmer, R.: An Introduction to the Theory of Neural Computation. Addison Wesley Publ. Comp., Redwood City (1991)Google Scholar
  8. 8.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)Google Scholar
  9. 9.
    Jiao, L.C., Du, H.F., Liu, F., Gong, M.G.: Immune Optimal Computation, Learning and Recognition. Science Press, Peking (2006) ISBN 978-7-03-017006-4Google Scholar
  10. 10.
    Timmis, J., Neal, M., Hunt, J.: An Artificial Immune System for Data Analysis. Biosystem 55(1/3), 143–150 (2000)CrossRefGoogle Scholar
  11. 11.
    Zhang, L., Zhong, Y., Huang, B., Li, P.: A resource limited artificial immune system algorithm for supervised classification of multi/hyper-spectral remote sensing imagery. International Journal of Remote Sensing 28(7-8), 1665–1686 (2007)CrossRefGoogle Scholar
  12. 12.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn., p. 311. John Wiley & Sons, New York (2001)zbMATHGoogle Scholar
  13. 13.
    Michalewicz, Z.: Genetic Algorithms +Data Structure=Evolution programs. Springer, New York (1992)Google Scholar
  14. 14.
    Kukolich, L., Lippmann, R.: LNKnet User’s Guide. Revision 4, MIT Lincoln Laboratory (February 2004),

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Xiaoyang Fu
    • 1
  • Shuqing Zhang
    • 2
  • Zhenping Pang
    • 1
  1. 1.College of Computer Science and TechnologyJilin UniversityZhuhaiChina
  2. 2.Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchunChina

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