Differential Evolution (DE) has been widely used in various application fields.
For solving high-cost problems, it needs to reduce the number of function evaluations more.
There are many studies on reducing function evaluations by constructing an approximation model and optimizing problems using approximate values.
However, it is difficult to learn proper approximation model which has enough generalization ability, and it needs much time to learn the model.
In this study, Estimated Comparison Method is proposed, where function evaluations are efficiently reduced even when an approximation model with low accuracy is used.
In the method, a comparison which compares approximate or estimated values is introduced and margin for error is given to cope with estimation error.
The potential model, which is an approximation model with low accuracy and does not need to learn model parameters, is used for approximation.
To show the advantage of the estimated comparison method, DE with estimated comparison is compared to DE and saving MGG which is an extension of minimal generation gap (MGG) for reducing the number of function evaluations.