  A phylogeny describes the evolutionary history of an evolving population.
Evolutionary search algorithms can perfectly track the ancestry of candidate
solutions, illuminating a population's trajectory through the search space.
However, phylogenetic analyses are typically limited to post-hoc studies of
search performance. We introduce phylogeny-informed subsampling, a new class of
subsampling methods that exploit runtime phylogenetic analyses for solving
test-based problems. Specifically, we assess two phylogeny-informed subsampling
methods -- individualized random subsampling and ancestor-based subsampling --
on three diagnostic problems and ten genetic programming (GP) problems from
program synthesis benchmark suites. Overall, we found that phylogeny-informed
subsampling methods enable problem-solving success at extreme subsampling
levels where other subsampling methods fail. For example, phylogeny-informed
subsampling methods more reliably solved program synthesis problems when
evaluating just one training case per-individual, per-generation. However, at
moderate subsampling levels, phylogeny-informed subsampling generally performed
no better than random subsampling on GP problems. Our diagnostic experiments
show that phylogeny-informed subsampling improves diversity maintenance
relative to random subsampling, but its effects on a selection scheme's
capacity to rapidly exploit fitness gradients varied by selection scheme.
Continued refinements of phylogeny-informed subsampling techniques offer a
promising new direction for scaling up evolutionary systems to handle problems
with many expensive-to-evaluate fitness criteria.
