Extremal Optimization Combined with LM Gradient Search for MLP Network Learning
Yu‐Wang Chen, Peng Chen, Yong-Zai Lu · International Journal of Computational Intelligence Systems · 2010
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 and 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.