In genetic algorithms, we don't usually know the way to solve the problem. We borrow this idea from nature of trying a lot of different solutions and slightly modifying them when we think they're getting closer to the actual solution. It's pretty co it anin. Now i save the craziest ones to ask you about for last, because i look through the examples and it's still kind of sketchy in my mind. But it's very interesting. That's the whole category of genetic algorithms, where you turn loose a bunch of different algrithms,. And then they tweak themselves, and they evolve to try to find the solution.