In many optimization heuristics there are a number of parameters to be chosen. These parameters typically have a crucial impact on the performance of the algorithm. It is therefore of great interest to set these parameters wisely. Unfortunately, determining the optimal parameter choices for a randomized search heuristic via mathematical means is a rather difficult task. Even worse, for many problems the optimal parameter choices seem to change during the optimization process. While this seems quite intuitive, little theoretical evidence exist to support this claim. In a series of recent works we have proposed two very simple success-based update rules for the parameter settings of some standard search heuristics. For both these rules we can prove that they yield a better performance than any static parameter choice. Based on joint work with Benjamin Doerr (Ecole Polytechnique), Timo Koetzing (HPI Potsdam, Germany), and Jing Yang (Ecole Polytechnique).