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.
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 ;)