Interesting news on genetic algorithms and a quick analysis of their academic/design impact upon those (and many other) areas. See here.
As you may see, there are persons which consider genetic algorithms too slow and others think they are too cute. :)
Some excerpts from the news:
"Yet there are drawbacks to the method. Although genetic algorithms have been applied with fantastic success in some cases, they fall short of being universal problem-solvers. And although evolutionary eons can be compressed into hours, finding the precise settings that will give a good solution can take months. How do you figure out which solutions are best? How often do you mutate the offspring? Computer scientists are reduced to trial-and-error knob-twiddling to get the right conditions."
"They’re too cute. Genetic algorithms don’t get bonus credibility just because that’s what nature did,"
"They are quite slow, and they require quite a bit of fiddling,"
"Genetic algorithms’ main contribution today are that they opened the door to biologically-inspired methods. The beauty and success of genetic algorithms motivated other computer scientists to look to biology for inspiration,"
By the way, I consider that genetic algorithms (and evolutionary algorithms in general) are very nice to solve problems, mainly when taking into account the lack of problem informations, such as no derivative available and/or so many constraints to handle.