Very true - there are lots of what the AI people call hyper parameters to tweak. And they do affect the balance between fast convergence and avoidance of local

Very true - there are lots of what the AI people call hyper parameters to tweak. And they do affect the balance between fast convergence and avoidance of local minima traps, as you would expect. Once you have used them for a while, you start to get a feel - if the optimization is rapidly converging to a poor solution, you need more variation - more mutation, bigger population, etc. if the solution is converging very slowly, you need Less variation. I like to set up the functions as Gaussian, then the parameter controlling the variation is a well understood sigma.