Although researchers have successfully incor- porated metamodels in evolutionary algorithms to solve computational-expensive optimization problems, they have scarcely performed comparisons among different metamod- eling techniques. This paper presents an in-depth comparison study over four of the most popular metamodeling tech- niques: polynomial response surface, Kriging, radial basis function neural network (RBF), and support vector regres-
sion. We adopted six well-known scalable test functions and performed experiments to evaluate their suitability to be cou- pled with an evolutionary algorithm and the appropriateness to surrogate problems by regions (instead of surrogating the entire problem). Notwithstanding that most researchers have undertaken accuracy as the main measure to discern among metamodels, this paper shows that the precision, measured with the ranking preservation indicator, gives a more valuable information for selecting purposes. Additionally, nonetheless each model has its own peculiarities; our results concur that RBF fulfills most of our interests. Furthermore, the readers can also benefit from this study if their problem at hand has certain characteristics such as a low budget of computational time or a low-dimension problem since they can assess spe- cific results of our experimentation.