Across the wide variety of chemical problems probed by GA searches in recent years, the most common method of ending a run is by simply having the GA terminate after completing a predetermined number of generations. The strategy most often employed is to run the GA for a set number of generations and stop the GA at that point, declaring it "converged" as demonstrated in the schematic in Figure2. The number of generations is set at a level thought to be substantially greater than the number needed to reach a plateau where the top performer ceases to change for many generations. This approach is common because it generally works and is easy to implement, but it has several drawbacks. First, this approach requires an initial guess of the approximate number of generations necessary to reach convergence, resulting in initial runs that may either have to be run for hundreds of generations more than necessary or be actively monitored by the researcher to determine when to manually stop the GA. Even once a good estimate of the number of generations necessary to reach convergence has been established, the GA must be set to complete a substantial number of additional generations to ensure that the random variability of each run is taken into account and the GA does not terminate prematurely; this can result in wasted resources as a GA runs for far longer than necessary. Finally, this predetermined endpoint approach is not easily transferable between GA methods for different applications. Whenever the optimization target changes (for example, choosing to optimize a different chemical property or set of properties), or even when the hyperparameters of a GA are changed, the number of generations needed for convergence can be impacted. This means that new initial guesses are needed and it is difficult to compare the endpoints of GAs without identical parameters due to the lack of a standard convergence protocol.