  Tuning parameters is an important step for the application of metaheuristics
to problem classes of interest. In this work we present a tuning framework
based on the sequential optimization of perturbed regression models. Besides
providing algorithm configurations with good expected performance, the proposed
methodology can also provide insights on the relevance of each parameter and
their interactions, as well as models of expected algorithm performance for a
given problem class, conditional on the parameter values. A test case is
presented for the tuning of six parameters of a decomposition-based
multiobjective optimization algorithm, in which an instantiation of the
proposed framework is compared against the results obtained by the most recent
version the Iterated Racing (Irace) procedure. The results suggest that the
proposed approach returns solutions that are as good as those of Irace in terms
of mean performance, with the advantage of providing more information on the
relevance and effect of each parameter on the expected performance of the
algorithm.
