ODE-LM: A hybrid training algorithm for feedforward neural networks

A hybrid training algorithm named ODE-LM, in which the orthogonal differential evolution (ODE) algorithm is combined with the Levenberg-Marquardt (LM) method, is proposed to optimize feedforward neural network weights and biases. The ODE is first applied to globally optimize the network weights in a large space to some extent (the ODE will stop after a certain generation), and then LM is used to further learn until the maximum number of iterations is reached. The performance of ODE-LM has been evaluated on several benchmarks. The results demonstrate that ODE-LM is capable to overcome the slow training of traditional evolutionary neural network with lower learning error. © Springer-Verlag Berlin Heidelberg 2014.