Evolving neural network ensembles using variable string genetic algorithm for Pattern Classification

In this paper, an evolving neural network ensembles (ENNE) classifier using variable string genetic algorithm (VGA) is proposed. For neural network ensembles (NNE) with regularized negative correlation learning (RNCL) algorithm, the two improvements are adopted: The first term is to evolve the appropriate architecture and initial connection weights of NNE using VGA algorithm, the second term is to optimize automatically the regularization parameter based on gradient descent while evolving the NNE´s weights. The effectiveness of ENNE classifier is demonstrated on a number of benchmark data sets. Compared with back-propagation algorithm multilayer perception (BP-MLP) classifier and NNE classifier with RNCL algorithm, it has shown that the ENNE classifier with VGA and RNCLgd hybrid algorithm has better classification performance.