In recent decades, metaheuristics have become increasingly popular as a tool for solving a large class of difficult optimization problems. However, determining the best configuration of a metaheuristic, which includes the program flow and the parameter settings, remains a difficult task. Adaptive metaheuristics (that change their configuration during the search) and multi-level metaheuristics (that change their configuration during the search by means of a metaheuristic) can be a solution for this. This chapter intends to give a quick review of the latest trends in adaptive metaheuristics and multi-level metaheuristics.