Genetic Algorithm (GA) has superiority over the classical optimization algorithms in finding the optimal solution in multi-parameter search space. Despite this superiority, it suffers from using an arbitrary selection of the binary length of each gene string that represents a single parameter in a chromosome (solution). The length of each gene depends not only on the lower and upper limits of the parameter to be optimized, but also on the resolution required. If the resolution is too coarse, the GA may never be able to find optima simply. This paper introduces an enhanced version of GAs (EGA) that uses a set of real parameters' values to represent a chromosome instead of a set of binary codes. It also proposes a control scheme based on the EGA for controlling dynamic processes at real-time. The proposed scheme can control a complex industrial process with time constant less than 2 ms.