"If the line splitter tries, like, 5,000 solutions and still hasn't found a winner yet, it just picks the best it found so far and bails."
This sounds like it would give no more guarantees of a deterministic result than AI methods would. They also try their best, only search a subset of the search space, and bail when user-defined criteria such as time, iteration, or space limits are reached.
Another approach could be to use both the author's hand-crafted algorithm and an AI method. There are at least two ways to go about that. The first would be to run the hand-crafted algorithm and pass its evaluation of the best solutions to seed an AI method (for example, as the seed populations in an evolutionary algorithm). The second would be to do the opposite, and run the AI method first, and seed the hand-crafted algorithm with the best solutions the AI method discovered.
Given that, I don't see any reason why the algorithm should be non-deterministic just because it gives up after a set number of iterations. As long every time it sees a given string it tries the possibilities in the same order, scores them the same way, and stops at the same place then it should be deterministic.
You're right that if the algorithm described in the article will always get the same result given the same input it will be deterministic in a way that the kinds of AI methods I'm thinking of (the stochastic kind) will not. That sort of non-determinism will be a problem if you expect your code to be formatted the same way every time it goes through the formatter.