  We present an analysis of the loss of population-level test coverage induced
by different down-sampling strategies when combined with lexicase selection. We
study recorded populations from the first generation of genetic programming
runs, as well as entirely synthetic populations. Our findings verify the
hypothesis that informed down-sampling better maintains population-level test
coverage when compared to random down-sampling. Additionally, we show that both
forms of down-sampling cause greater test coverage loss than standard lexicase
selection with no down-sampling. However, given more information about the
population, we found that informed down-sampling can further reduce its test
coverage loss. We also recommend wider adoption of the static population
analyses we present in this work.
