The answer of Steve Okay is correct, but I'm not sure if it really answers the question to the necessary degree.
It is important to focus on the first part:
A genetic algorithm is a class of [...] algorithms.
That means they act in boundaries that can be described exactly. It's only about searching good parameters / combinations.
Differences to evolution by natural selection are:
- Fitness function: in natural selection, this is nature itself. On the most basic level the laws of physics, on higher abstraction levels chemistry and biology. For the algorithms, it is whatever the developer thinks fits to the problem. It might be a bad choice that steers development in the wrong direction
- Sample sizes: think of how many bacteria there were until the first mammal developed. You will not even be close to the order of magnitude in a computer program
- Breeder selection: see fitness function
- Mutation: see fitness function; also, the amount of possible mutations is way bigger than in the algorithms.
- Generations/time: similar as with the sample size; nature has a head start of a few billion years. And, after all, our computers are just one part of nature (aka our universe)
Now I would like to answer in a different direction: evolutionary algorithms are just one tribe of machine learning. And one which is not successful as well. The last publication I read from this branch was about 3 years ago so please correct me if something happened I'm not aware of. But at the time they couldn't even get MNIST to 90%, where a beginners tutorial on deep learning already gets to 95% with cheap hardware in less than one hour. So while they might be able to search through a lot of possible solutions, I'd rather invest the money you would spend on computational power in a deep learning working student. The student will be faster and cheaper.