  Choosing a suitable algorithm from the myriads of different search heuristics
is difficult when faced with a novel optimization problem. In this work, we
argue that the purely academic question of what could be the best possible
algorithm in a certain broad class of black-box optimizers can give fruitful
indications in which direction to search for good established optimization
heuristics. We demonstrate this approach on the recently proposed DLB
benchmark, for which the only known results are $O(n^3)$ runtimes for several
classic evolutionary algorithms and an $O(n^2 \log n)$ runtime for an
estimation-of-distribution algorithm. Our finding that the unary unbiased
black-box complexity is only $O(n^2)$ suggests the Metropolis algorithm as an
interesting candidate and we prove that it solves the DLB problem in quadratic
time. Since we also prove that better runtimes cannot be obtained in the class
of unary unbiased algorithms, we shift our attention to algorithms that use the
information of more parents to generate new solutions. An artificial algorithm
of this type having an $O(n \log n)$ runtime leads to the result that the
significance-based compact genetic algorithm (sig-cGA) can solve the DLB
problem also in time $O(n \log n)$ with high probability. Our experiments show
a remarkably good performance of the Metropolis algorithm, clearly the best of
all algorithms regarded for reasonable problem sizes.
