There are many fantastic applications of genetic algorithms. Conceivably my favorite is their usage in evaluating Jazz melodies done as part of a PhD project in Edinburgh.
However the one we look at here is chosen because it demonstrates how a fairly lightweight effort using GAs can often be highly effective. Hence in their paper "The Application of Artificial Intelligence to Transportation System Design", Ricardo Hoar and Joanne Penner explain their undergraduate project that involved representing vehicles on a road system as autonomous agents, and utilizing. In fact GA approaches to evolve solutions to the timing of traffic lights to increase there the traffic flow in the system. Therefore the optimum settings for where lights come on and go off is known only for very simple situations, so an AI-style search can be used to try and find good solutions. Hoar and Penner chose to do this in an evolutionary fashion. They don't give details of the representation scheme they used, but traffic light times are real-valued numbers, so they could have used a bit-string representation.
Therefore the evaluation function they utilize involved the total waiting time and total driving time for each car in the system as follows like:
Hence the results they produced were good as worthy of writing a paper. So the two graphs below describe the decrease in overall waiting time for a simple road and for a more complicated road as albeit not amazingly complicated.
We see that in both cases, the waiting time has roughly halved, which is a good result. In the first case, for the simple road system, the GA evolved a solution verysimilar to the ones worked out to be optimal by humans. We see that GAs can be used to find good near-optimal solutions to problems where a more cognitive approach might have failed as i.e., humans still can't work out how best to tune traffic light times, but a computer can evolve a good solution.