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.
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