  Algorithm selection wizards are effective and versatile tools that
automatically select an optimization algorithm given high-level information
about the problem and available computational resources, such as number and
type of decision variables, maximal number of evaluations, possibility to
parallelize evaluations, etc. State-of-the-art algorithm selection wizards are
complex and difficult to improve. We propose in this work the use of automated
configuration methods for improving their performance by finding better
configurations of the algorithms that compose them. In particular, we use
elitist iterated racing (irace) to find CMA configurations for specific
artificial benchmarks that replace the hand-crafted CMA configurations
currently used in the NGOpt wizard provided by the Nevergrad platform. We
discuss in detail the setup of irace for the purpose of generating
configurations that work well over the diverse set of problem instances within
each benchmark. Our approach improves the performance of the NGOpt wizard, even
on benchmark suites that were not part of the tuning by irace.
