Extremal Optimization Combined with LM Gradient Search for MLP Network Learning

@article{Chen2010ExtremalOC,
  title={Extremal Optimization Combined with LM Gradient Search for MLP Network Learning},
  author={Peng Chen and Yongzai Lu and Yu-Wang Chen},
  journal={Int. J. Comput. Intell. Syst.},
  year={2010},
  volume={3},
  pages={622-631}
}
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… 

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