The Biased Random-Key Genetic Algorithm (BRKGA) is a populationbased metaheuristic applied to obtain optimal or near-optimal solutions to combinatorial problems. To ensure the good performance of this algorithm (and other metaheuristics in general), defining parameter settings is a crucial step. Parameter values have a great influence on determining whether a good solution will be found by the algorithm and whether the search process will be efficient. One way of tackling the parameter setting problem is through the parameter control (or online tuning) approach. Parameter control allows the algorithm to adapt parameter values according to different stages of the search process and to accumulate information on the fitness landscape during the search to use this information in later stages. It also releases the user from the task of defining parameter settings, implicitly solving the tuning problem. In this work, we evaluate two strategies to implement parameter control in BRKGA. Our first approach was adopting random parameter values for each of BRKGA s generations. The second approach was to introduce the principles adopted by Iterated Race, a state-of-the-art tuning method, to BRKGA. Both strategies were evaluated in three classical optimization problems (Flowshop Permutation Problem, Set Covering Problem, and the Traveling Salesman Problem) and led to competitive results when compared to the tuned algorithm.