Genetic algorithms have the ability to capture the imagination of public and computer science students because they can find very messy and random solutions if you run them long enough.
In the general context of search and optimization algorithms they are not impressive. When you can't use anting better, like Mote Carlo or simulated annealing, evolutionary algorithms are often the last hope before brute forcing it. GA can be very impressive when you can restrict the search space and find good representation for the problem.
Evolutionary algorithms and swarm optimization algorithms are the next step above brute force and random search. In the space of optimization algorithms are below everything else.
Usually they are the last option. Using them to locate software defects means that you have no special insight into software defects but you hope to generate something better than random.
Additionally it is the one algorithm that has produced the human brain and the one algorithm that has produced biological nanomachines that move and think. No other algorithm has done such a feat. It is naive to dismiss such an algorithm as "overfitting" when the incredible results are all around you.
It seems that evolution had not merely selected the best code for the task, it had also advocated those programs which took advantage of the electromagnetic quirks of that specific microchip environment. The five separate logic cells were clearly crucial to the chip’s operation, but they were interacting with the main circuitry through some unorthodox method— most likely via the subtle magnetic fields that are created when electrons flow through circuitry, an effect known as magnetic flux."
This is absolutely incredible. Makes you wonder how much potential the real world has compared to the simulated environment usually used to test theoretical solutions.