  Parent selection plays an important role in evolutionary algorithms, and many
strategies exist to select the parent pool before breeding the next generation.
Methods often rely on average error over the entire dataset as a criterion to
select the parents, which can lead to an information loss due to aggregation of
all test cases. Under epsilon-lexicase selection, the population goes to a
selection pool that is iteratively reduced by using each test individually,
discarding individuals with an error higher than the elite error plus the
median absolute deviation (MAD) of errors for that particular test case. In an
attempt to better capture differences in performance of individuals on cases,
we propose a new criteria that splits errors into two partitions that minimize
the total variance within partitions. Our method was embedded into the FEAT
symbolic regression algorithm, and evaluated with the SRBench framework,
containing 122 black-box synthetic and real-world regression problems. The
empirical results show a better performance of our approach compared to
traditional epsilon-lexicase selection in the real-world datasets while showing
equivalent performance on the synthetic dataset.
