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

Abstract

AbstractA 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.Keywordsresource limited immune approach (RLIA)evolutionary artificial neural network (EANN)pattern classification