Yea - that is related to genetic programming. That, and using auto-encoders for e.g. image compression are known approaches in "AI".

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 ;)

> 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.

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...

Yea I am aware of meta hyperparameter approach for ML, except they only focus on accuracy instead of also including train/prediction times in to the equation :) That's what I was referring to! (you can save A LOT of compute and zoom in on things that work if you can weed out slow / badly performing algorithms as part of meta learning hyperparameters).

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.