Abstract: Randomized optimization heuristics, such as Simulated Annealing or evolutionary algorithms, are applied very successfully to a wide range of complex optimization problems. Key to the success of each heuristic is its distribution from which it draws new candidate solutions. Modern heuristics use feedback from prior solutions to dynamically adjust their distribution during the optimization process. Such update strategies range from rather simple success-based rules to complex strategies combining static as well as dynamic information. Similar approaches can be found in Reinforcement Learning, however applied to dynamic optimal control problems. While several types of evolutionary algorithms can be used for their solution, the field generated its own approaches to learning optimal behavior as well.