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Genetic Programming (GP) often uses large training sets and requires allindividuals to be evaluated on all training cases during selection. Randomdown-sampled lexicase selection evaluates individuals on only a random subsetof the training cases allowing for more individuals to be explored with thesame amount of program executions. However, creating a down-sample randomlymight exclude important cases from the current down-sample for a number ofgenerations, while cases that measure the same behavior (synonymous cases) maybe overused despite their redundancy. In this work, we introduce InformedDown-Sampled Lexicase Selection. This method leverages population statistics tobuild down-samples that contain more distinct and therefore informativetraining cases. Through an empirical investigation across two different GPsystems (PushGP and Grammar-Guided GP), we find that informed down-samplingsignificantly outperforms random down-sampling on a set of contemporary programsynthesis benchmark problems. Through an analysis of the created down-samples,we find that important training cases are included in the down-sampleconsistently across independent evolutionary runs and systems. We hypothesizethat this improvement can be attributed to the ability of Informed Down-SampledLexicase Selection to maintain more specialist individuals over the course ofevolution, while also benefiting from reduced per-evaluation costs.

Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving