Many real-world optimization challenges are significantly harder than the scenarios that can be rigorously analyzed by mathematical means. In addition, practitioners often lack time and resources to develop problem-specific solution strategies, they therefore need to rely on general-purpose optimization heuristics such as local search variants, evolutionary and genetic algorithms, Bayesian optimization techniques, and similar. Unfortunately, practitioners often choose and apply these algorithms without much guidance from the scientific community.With this presentation, we will discuss how (theoretical and empirical) research can be used to develop more principled usage of these randomized optimization heuristics.