  In genetic programming, an evolutionary method for producing computer
programs that solve specified computational problems, parent selection is
ordinarily based on aggregate measures of performance across an entire training
set. Lexicase selection, by contrast, selects on the basis of performance on
random sequences of training cases; this has been shown to enhance
problem-solving power in many circumstances. Lexicase selection can also be
seen as better reflecting biological evolution, by modeling sequences of
challenges that organisms face over their lifetimes. Recent work has
demonstrated that the advantages of lexicase selection can be amplified by
down-sampling, meaning that only a random subsample of the training cases is
used each generation. This can be seen as modeling the fact that individual
organisms encounter only subsets of the possible environments, and that
environments change over time. Here we provide the most extensive benchmarking
of down-sampled lexicase selection to date, showing that its benefits hold up
to increased scrutiny. The reasons that down-sampling helps, however, are not
yet fully understood. Hypotheses include that down-sampling allows for more
generations to be processed with the same budget of program evaluations; that
the variation of training data across generations acts as a changing
environment, encouraging adaptation; or that it reduces overfitting, leading to
more general solutions. We systematically evaluate these hypotheses, finding
evidence against all three, and instead draw the conclusion that down-sampled
lexicase selection's main benefit stems from the fact that it allows the
evolutionary process to examine more individuals within the same computational
budget, even though each individual is examined less completely.

Neural and Evolutionary Computing;Artificial Intelligence