> They are very prone to getting stuck in local minima.

That's quite a generalization. A GA's tendency to get stuck in local minima can be mitigated by adjusting population size, selection method/size and rate of mutation -- i.e. increase the randomness of the search.

This is not a good generalization. I've usually only seen this issue with optimization problems when:

1) You haven't played with parameters 2) Implementation is not correct (usually the case with genetic algos, since it requires a reasonable amount of domain expertise vs say GD)

Copped out when called out. Deleted comment said GAs were bad search algos and tend to get stuck at local minima.

What evidence is informing your opinion that genetic algorithms are a bad search algorithm? What makes you say that they are very prone to getting stuck in local minima? Do you think they suffer from local minima more than, say, gradient descent?