  Phylogenies (ancestry trees) depict the evolutionary history of an evolving
population. In evolutionary computing, a phylogeny can reveal how an
evolutionary algorithm steers a population through a search space, illuminating
the step-by-step process by which any solutions evolve. Thus far, phylogenetic
analyses have primarily been applied as post-hoc analyses used to deepen our
understanding of existing evolutionary algorithms. Here, we investigate whether
phylogenetic analyses can be used at runtime to augment parent selection
procedures during an evolutionary search. Specifically, we propose
phylogeny-informed fitness estimation, which exploits a population's phylogeny
to estimate fitness evaluations. We evaluate phylogeny-informed fitness
estimation in the context of the down-sampled lexicase and cohort lexicase
selection algorithms on two diagnostic analyses and four genetic programming
(GP) problems. Our results indicate that phylogeny-informed fitness estimation
can mitigate the drawbacks of down-sampled lexicase, improving diversity
maintenance and search space exploration. However, the extent to which
phylogeny-informed fitness estimation improves problem-solving success for GP
varies by problem, subsampling method, and subsampling level. This work serves
as an initial step toward improving evolutionary algorithms by exploiting
runtime phylogenetic analysis.
