Aging is a general mechanism that some randomized search heuristics employ to increase the diversity of their collection of search points. A more diverse collection of search points is believed to improve the search heuristic’s performance for difficult problems. The most prominent randomized search heuristics with aging are evolutionary algorithms and artificial immune systems. While it is known that randomized search heuristics with aging can be very much more efficient than randomized search heuristics without aging the details of the origin of such benefits are difficult to understand. We contribute to this understanding by presenting a detailed and structured analysis of aging. We prove that in addition to diversity with respect to search points diversity with respect to age plays a key role. We analyze different ways of dealing with age diversity by means of theoretical as well as empirical analyses. Major results include a more structured understanding of aging and showcases where age diversity can make the difference between efficient and completely inefficient optimization.