abstract = "Phylogenies (ancestry trees) tell the evolutionary
history of an evolving population. In evolutionary
computing, phylogenies reveal how evolutionary
algorithms steer populations through a search space by
illuminating the step-by-step evolution of solutions.
To date, phylogenetic analyses have almost exclusively
been applied in post hoc analyses of evolutionary
algorithms for performance tuning and research. Here,
we apply phylogenetic information at runtime to augment
parent selection procedures that use training sets to
assess candidate solution quality. We propose
phylogeny-informed fitness estimation, thinning a
fraction of costly training case evaluations by
substituting the fitness profiles of near relatives as
a heuristic estimate. 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, sub-sampling method, and subsampling level.
This work serves as an initial step toward improving
evolutionary algorithms by exploiting runtime
phylogenetic analysis.",