Hyper parameter optimization is widely used in AI areas. Hyper parameter usually means the value controls the whole learning process, but itself cannot be learned or tunned in training process. Hyper parameter is very important because it will greatly affect the learning result. A good hyper parameter set can lead to a much better result or cost much less training time, instead a bad hyper parameter usually will end in local optimum, or even failed to converge. Hyper parameters can be many difference kinds of types, it could be in the model itself (depth, node counts, etc..), or it could be in the algorithm (learning rate, optimizer, etc..). Different models or algorithms usually need different hyper parameters, even the same model/algorithm can use different hyper parameters to achieve better results. So hyper parameter exists in different part of the training process, some of the hyper parameter is described in a category. It usually means that the parameter can only be chosen in a range. This kind of parameter has some properties, for this special kind of hyper parameter we proposed a common method here to optimize it. By using this method we turn the category problems into Real searching space to achieve a better result.