> There are a few problems with this approach though. For one, there are often many local maxima that a genetic algorithm can easily converge on. > Secondly, t

> There are a few problems with this approach though. For one, there are often many local maxima that a genetic algorithm can easily converge on. > Secondly, the state space is often enormous, and testing/discarding millions of generations of poorly constructed networks is a very inefficient way of sampling it. This is true for the living as well.