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This paper presents an adaptive evolution control based on the feasibility of solutions, which is used with the nearest-neighbor regression surrogate model, to approximate the objective function value and the sum of constraint violation when solving constrained numerical optimization problems. The search algorithm used is the "differential evolution with combined variants'' (DECV) and the constrainthandling technique adopted is the set of feasibility rules. The approach is compared against one state-of-the-art algorithm that employs the same surrogate model with an adaptive evolution control, as well. Twenty-four well-known test problems are solved in the experiments. From the obtained results, it is found that the evolution control based on the feasibility of solutions reduces the number of evaluations in the expensive model, particularly in problems with inequality constraints.