  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.
