  It is generally accepted that "diversity" is associated with success in
evolutionary algorithms. However, diversity is a broad concept that can be
measured and defined in a multitude of ways. To date, most evolutionary
computation research has measured diversity using the richness and/or evenness
of a particular genotypic or phenotypic property. While these metrics are
informative, we hypothesize that other diversity metrics are more strongly
predictive of success. Phylogenetic diversity metrics are a class of metrics
popularly used in biology, which take into account the evolutionary history of
a population. Here, we investigate the extent to which 1) these metrics provide
different information than those traditionally used in evolutionary
computation, and 2) these metrics better predict the long-term success of a run
of evolutionary computation. We find that, in most cases, phylogenetic metrics
behave meaningfully differently from other diversity metrics. Moreover, our
results suggest that phylogenetic diversity is indeed a better predictor of
success.
