This paper presents a very simple surrogate optimization method - a Tolerance-based Surrogate Method. A surrogate optimization in general is essential to more and more frequently used optimization in the development process of new technologies. Fitness functions of such systems are often costly, therefore keeping a number of evaluations of the fitness functions at minimum is of a great importance in order to save computer and time resources, i.e. the overall cost of design. Unlike other complex surrogate optimization methods, the tolerance-based surrogate method does not require excessive computational resources, is easy to implement, and is flexible for all types of optimization algorithms. Behaviour of the tolerance-based surrogate method is demonstrated on several modified benchmark problems. Afterwards, our method is verified on a real-world time-demanding optimization task.
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