> Is there any mathematics on defining what % of the dataset should be training vs. testing or is it left to the analyst (like with confidence intervals (95% hypothesis testing etc.))?
I think you're thinking about this wrong. You need to set aside some of your data for testing/cross-validation, otherwise you have no way of knowing even if you have enough data to train your model! i.e. you want to get to the point where additional training data isn't getting you a better model, and if you're not at that point you should probably collect more data.
It seems I'm thinking of a prediction algorithm and being discussed is a genetic algorithm, for which I can understand the necessity of a training set and a test set.
Either way, my knowledge on machine learning is lacking, time to hit up Google.
I think you're thinking about this wrong. You need to set aside some of your data for testing/cross-validation, otherwise you have no way of knowing even if you have enough data to train your model! i.e. you want to get to the point where additional training data isn't getting you a better model, and if you're not at that point you should probably collect more data.