This is an advantage over some other methods for performing classification (eg. C4.5),
which will always produce the same classifier regardless of the amount of time available for training. Another advantage for GP is that each run is probabilistic and different runs attempting
to solve the same problem will virtually never produce the same result. Such variability of the final solution lends GP classifiers well to a voting strategy, which is often able to produce more accurate classification results.