The program at hand pretty much does that: Mutate all formulas, find the best and reward them. With some twists and turns.

This is all well known. It's also well known that "just finding the best" isn't always the best approach. It's an approach that will only work if in the field of all possible approximations, there's always a monotone path (that is, every next approximation is better) towards the ultimate goal. However, if that's the case, there's usual an efficient, direct way of solving the problem.

The interesting problems are when there aren't always monotone paths towards the end goal; that is, there are local maxima, and you may have to first decrease your current approximation to be able to further increase it.

Unfortunally, your description isn't clear whether your technique does such a thing.

Abigail

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