  Dynamic Algorithm Configuration (DAC) tackles the question of how to
automatically learn policies to control parameters of algorithms in a
data-driven fashion. This question has received considerable attention from the
evolutionary community in recent years. Having a good benchmark collection to
gain structural understanding on the effectiveness and limitations of different
solution methods for DAC is therefore strongly desirable. Following recent work
on proposing DAC benchmarks with well-understood theoretical properties and
ground truth information, in this work, we suggest as a new DAC benchmark the
controlling of the key parameter $\lambda$ in the
$(1+(\lambda,\lambda))$~Genetic Algorithm for solving OneMax problems. We
conduct a study on how to solve the DAC problem via the use of (static)
automated algorithm configuration on the benchmark, and propose techniques to
significantly improve the performance of the approach. Our approach is able to
consistently outperform the default parameter control policy of the benchmark
derived from previous theoretical work on sufficiently large problem sizes. We
also present new findings on the landscape of the parameter-control search
policies and propose methods to compute stronger baselines for the benchmark
via numerical approximations of the true optimal policies.
