Extremal Optimization Combined with LM Gradient Search for MLP Network Learning

Peng Chen, Yong-Zai Lu, Yu-Wang Chen
2010 International Journal of Computational Intelligence Systems  
Gradient search based neural network training algorithm may suffer from local optimum, poor generalization and slow convergence. In this study, a novel Memetic Algorithm based hybrid method with the integration of "extremal optimization" and "Levenberg-Marquardt" is proposed to train multilayer perceptron (MLP) networks. Inheriting the advantages of the two approaches, the proposed "EO-LM" method can avoid local minima and improve MLP network learning performance in generalization capability
more » ... computation efficiency. The experimental tests on two benchmark problems and an application example for the end-point-prediction of basic oxygen furnace in steelmaking show the effectiveness of the proposed EO-LM algorithm.
doi:10.1080/18756891.2010.9727728 fatcat:u4qkiq27onaetf7hlr54oybzty