  The domain of an optimization problem is seen as one of its most important
characteristics. In particular, the distinction between continuous and discrete
optimization is rather impactful. Based on this, the optimizing algorithm,
analyzing method, and more are specified. However, in practice, no problem is
ever truly continuous. Whether this is caused by computing limits or more
tangible properties of the problem, most variables have a finite resolution.
  In this work, we use the notion of the resolution of continuous variables to
discretize problems from the continuous domain. We explore how the resolution
impacts the performance of continuous optimization algorithms. Through a
mapping to integer space, we are able to compare these continuous optimizers to
discrete algorithms on the exact same problems. We show that the standard
$(\mu_W, \lambda)$-CMA-ES fails when discretization is added to the problem.
