I'm looking at how I currently evaluate my classification models and wondering if it could be improved. I've got a stochastic algorithm (Genetic Programming), which for non-classification problems is run ~50 times on each problem to get an overview for how it performs.
Currently I'm implementing repeated 10-fold stratified cross-validation. For standard deterministic modeling techniques this assesses the algorithm over various splits of the data. With my GP classifiers I have an additional source of variability alongside variability due to how the dataset was split; as the RNG seed for each run will be different.
Is repeated 10-fold cross-validation a useful measure of my model's performance or would it be better to use a different technique such as bootstrapping and run this multiple times? An alternative approach would be to use repeated CV with 50 repeats, but make each repeat use the same folds. Therefore I've got the CV reducing variance due to the splitting of the data folds, and the 50 repeats minimising variation from the stochastic model. However this would be very computationally expensive!