  Modern optimization strategies such as evolutionary algorithms, ant colony
algorithms, Bayesian optimization techniques, etc. come with several parameters
that steer their behavior during the optimization process. To obtain
high-performing algorithm instances, automated algorithm configuration
techniques have been developed. One of the most popular tools is irace, which
evaluates configurations in sequential races, making use of iterated
statistical tests to discard poorly performing configurations. At the end of
the race, a set of elite configurations are selected from those survivor
configurations that were not discarded, using greedy truncation selection. We
study two alternative selection methods: one keeps the best survivor and
selects the remaining configurations uniformly at random from the set of
survivors, while the other applies entropy to maximize the diversity of the
elites. These methods are tested for tuning ant colony optimization algorithms
for traveling salesperson problems and the quadratic assignment problem and
tuning an exact tree search solver for satisfiability problems. The
experimental results show improvement on the tested benchmarks compared to the
default selection of irace. In addition, the obtained results indicate that
non-elitist can obtain diverse algorithm configurations, which encourages us to
explore a wider range of solutions to understand the behavior of algorithms.
