  The gradient information of multilayer perceptron with a linear neuron is
modified with functional derivative for the global minimum search benchmarking
problems. From this approach, we show that the landscape of the gradient
derived from given continuous function using functional derivative can be the
MLP-like form with ax+b neurons. In this extent, the suggested algorithm
improves the availability of the optimization process to deal all the
parameters in the problem set simultaneously. The functionality of this method
could be improved through intentionally designed convex function with
Kullack-Liebler divergence applied to cost value as well.
