  The growing volume of data makes the use of computationally intense machine
learning techniques such as symbolic regression with genetic programming more
and more impractical. This work discusses methods to reduce the training data
and thereby also the runtime of genetic programming. The data is aggregated in
a preprocessing step before running the actual machine learning algorithm.
K-means clustering and data binning is used for data aggregation and compared
with random sampling as the simplest data reduction method. We analyze the
achieved speed-up in training and the effects on the trained models test
accuracy for every method on four real-world data sets. The performance of
genetic programming is compared with random forests and linear regression. It
is shown, that k-means and random sampling lead to very small loss in test
accuracy when the data is reduced down to only 30% of the original data, while
the speed-up is proportional to the size of the data set. Binning on the
contrary, leads to models with very high test error.
