Feature selection in high-dimensional records is one of the energetic areas of research in model recognition. In view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enables to adequately decide which algorithm to use in certain situations.In this editorial, a new method for feature selection algorithm in high-dimensional data is anticipated that can control the trade-off between accuracy and classification time. This scheme is based on a greedy metaheuristic algorithm called greedy randomized adaptive search procedure (GRASP).It uses an extensive version of a simulated annealing (SA) algorithm for local search. In this version of SA, new parameters are fixed that allow the algorithm to control the trade-off between accuracy and classification time. New results show domination of the proposed method over previous versions of GRASP for feature selection. Also, they show how the trade-off between accuracy and classification time is convenient by the bounds introduced in the proposed method.