The finding above highlights a weakness in genetic algorithms as a whole, as they are limited due to the randomness inherit in their nature. In other words, GAs can find a local optima while sometimes missing the global one. We tried to avoid this problem by running the GA 10 times, but even so it is evident that the GA did miss some potential candidates. The likelihood of this happening can be lowered by running the GA for more generations and more times, but then the return-on-investment (ROI) might not be favorable if this takes longer and has higher computational costs.