Inadequate selection of operators and parameters will produce premature convergence that will be avoided by adopting finer setting of parameters. This research was inspired by the concept of quality control utilizing as a control mechanism for process control in various GA searching processes. The process controllers attempt to apply these mechanisms to optimize the settings of the operators and parameters. In this study, we explored the function of the active setting is to adjust the trade-off between the exploration and exploitation by twelve's searching divisions. It applies evolution evidence to supervise the active setting of the GA parameters. Three different kinds of theoretical test-beds were carried out to validate this approach. The test results have been discovered that the active setting can make better the performance of genetic algorithm search. The test consequences also are showed that active is superior to static setting.