There was, how ever, no machine learning or optimizing. Instead, he called it "prospecting" and just generate a new one from scratch each time until he found something interesting.
I'm particularly proud of this meta approach and I am actually thinking this could become huge: the same thing can be done for hyperparameter optimization in machine learning tasks.
Hyperparamter optimization is currently focused on minimizing cross-validation error, but using this concept you could have weights on accuracy, training time and prediction time (very similar to compression where the 3 dimensions are size, write time and read time), and then given a new unknown dataset you could predict what model/hyperparameters to use.
Maybe this should be patented ;)
There is already a substantial field of Machine Learning/Meta Learning which focuses on exactly this. For example, this paper [1] from NeurIPS 2015 does exactly what you suggest.
[1]: https://papers.nips.cc/paper/5872-efficient-and-robust-autom...
To make it extra clear: by doing a lot of compute on different datasets and not only recording the accuracy but also time it took, and then by including that as dimension it will even give better results.