Genetic Algorithms is a description for a wide range of techniques for adding some extra variety to search algorimhs. You maintain a population of candidate solutions and at each step you improve them by a combination of local search (like gradient descent or greedy neighbor search) and solution mixing (to help escape local minima). When doing this there are lots of ways to go about it. How to decide what population to keep, what local optimization to use, and how to mix the candidate optimizations.

If you look closely, all of these are not quite how biology works. In biology the population is decided by natural or artificial selection while in a GA there may be more factors at play (such as favoring a more diverse pool). The way solutions are represented is also different. In biology you have genes which behave according to the laws of genetics. In a GA, on the other hand, the solution representation and the operations to mutate and mix solutions are carefully planned by an intelligent designer.