I read somewhere online, how one shouldn't show their interest in genetic algorithms to a potential grad school, as its not the 'in' thing in todays CS community, compared to Machine Learning algorithms. I am interested in genetic algorithms and reading such a notion left me a little bothered, honestly.
When applying to a school you should be aware of the faculty at that school and the research they perform. Take a look at their research papers. If nobody at the school does work in genetic algorithms, then it probably isn't going to help you to mention your interest in the area.
If you have a sincere interest in genetic algorithms, then you need to find a school with researchers that are doing work in that area. If there is a prominent researcher at a school working on genetic algorithms, then you really want to mention your interest. If the researcher is accepting new students it might improve your chances of acceptance.
There are always hot areas in a field, and machine learning is currently hot, and genetic algorithms aren't as hot. But, there is good work being done in both fields. (There is also bad work being done in both fields.) I would advise you to find someone doing excellent work, and then the field that you are in will not matter as much.
I suggest having a quick look into combining genetic algorithms and neural networks, some work has been done in this area, perhaps there is more to do. The trend seems to be combining different machine learning techniques (e.g., deep reinforcement learning) and this is something the original poster could exploit to have their cake (research GAs) and eat it (submit to machine-learning conferences where neural nets are hot).
The question distinguishes between genetic algorithms and Machine Learning. Yet, a quick search on genetic algorithms and machine learning shows that the former may be (and often is) considered a sub-discipline of the latter. Given that the two have (some) overlap there may be an alternative.