Algorithm of neural network ensembles and robust learning

Abstract

Neural networks ensemble (NNE) has recently attracted great interests because of their advantages over single neural networks (SNN) as the ability of universal approximate and generalization. However, the design of neural network ensembles is a complex task. In this paper, we propose a general framework for designing neural network ensembles by means of cooperative co-evolution. The proposed model has two main objectives: first, the improvement of the combination of the trained individual networks; second, the cooperative evolution of such networks, encouraging collaboration among them, instead of a separate training of each network. In order to favor the cooperation of the networks, each network is evaluated throughout the evolutionary process using a PSO algorithm based on bootstrap technology (BPSO). A simulation example of the 3-D Mexican Hat is given to validate the method. The result proved its effectiveness. © 2009 Springer Berlin Heidelberg.