Genetic Programming (GP) often uses large training sets and requires all individuals to be
evaluated on all training cases during selection. Random down-sampled lexicase selection …

We present an analysis of the loss of population-level test coverage induced by different
down-sampling strategies when combined with lexicase selection. We study recorded …

Genetic programming systems often use large training sets to evaluate the quality of
candidate solutions for selection, which is often computationally expensive. Down-sampling …

In recent years, several new lexicase-based selection variants have emerged due to the
success of standard lexicase selection in various application domains. For symbolic …

Counterexample-driven genetic programming (CDGP) uses specifications provided as
formal constraints to generate the training cases used to evaluate evolving programs. It has …

Genetic programming systems typically require large computational resource investments
for training-set evaluations. Down-sampling these sets has proven to decrease costs and …

This short paper presents the main findings of our work titled Informed Down-Sampled
Lexicase Selection: Identifying Productive Training Cases for Efficient Problem Solving …