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

Mohammad Ibraim Sarker, Yali Nie, Hong Yongki, Hyongsuk Kim

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

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