A resource limited immune approach for evolving architecture and weights of multilayer neural network

Authors:
Xiaoyang Fu;Shuqing Zhang;Zhenping Pang
Affiliations:
College of Computer Science and Technology, Jilin University, Zhuhai, China;Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China;College of Computer Science and Technology, Jilin University, Zhuhai, China
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Year:
2010

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Abstract

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.