The uniformity penalty formula can probably be improved. This can be seen by looking at the log of resulting from running diversityUTest::test_diversity_5_parity_no_autoscale(). At the end of the first iteration, the best 2 candidates are true and false. However false already has a small uniformity penalty, which doesn't make much sense since its behavioral score is at maximum distance from the behavioral score of true, ideally it should be null.
The uniformity penalty formula can probably be improved. This can be seen by looking at the log of resulting from running
diversityUTest::test_diversity_5_parity_no_autoscale(). At the end of the first iteration, the best 2 candidates aretrueandfalse. Howeverfalsealready has a small uniformity penalty, which doesn't make much sense since its behavioral score is at maximum distance from the behavioral score oftrue, ideally it should be null.