Problem-solving benefits of down-sampled lexicase selection

TL;DR

Down-sampled lexicase selection in genetic programming improves problem-solving by enabling more individuals to be evaluated within the same computational budget, despite less thorough individual assessments.

Contribution

This paper provides the most extensive benchmarking of down-sampled lexicase selection and clarifies that its main benefit is increased population coverage rather than overfitting reduction or environmental variation.

Findings

Down-sampling enhances the number of individuals evaluated per generation.

The benefits are not due to overfitting or environmental change.

Main advantage is increased population coverage within the same computational budget.

Abstract

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.