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A new method to improve the performance of Random weight change (RWC)algorithm based on a simple genetic algorithm, namely, Genetic random weightchange (GRWC) is proposed. It is to find the optimal values of global minimavia learning. In contrast to Random Weight Change (RWC), GRWC contains aneffective optimization procedure which are good at exploring a large andcomplex space in an intellectual strategies influenced by the GA/RWC synergy.By implementing our simple GA in RWC we achieve an astounding accuracy offinding global minima.

Genetic Random Weight Change Algorithm for the Learning of Multilayer Neural Networks