Here’s the finished product, it can optimize N-dimensions, and you can subdivide them however you like. In the attached example, it’s broken into 3 variables and 3 variables, producing a curve in 3 space, but this could also be treated as a single six variable function, and the algorithm is indifferent. The original curve is on the left, the interpolated curve is on the right, and this took just over two minutes to run. The results are awesome. Note you could also use this to find a generalized goal state, as I’ve simply set this example up to find coefficients of a polynomial, but again, the method is generalized. As a consequence, it is a generalized N-Dimensional state space algorithm, that runs very quickly, and has a deterministic runtime, though you are of course not guaranteed any particular results.