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 …

Lexicase selection is a parent selection method that considers training cases individually,
rather than in aggregate, when performing parent selection. Whereas previous work has …

Most genetic programming systems use mutation and crossover operators to create child
programs from selected parent programs. Typically the mutation operator will replace a …

The lexicase parent selection method selects parents by considering performance on
individual data points in random order instead of using a fitness function based on an …

Lexicase selection has been proven highly successful for finding effective solutions to
problems in genetic programming, especially for test-based problems where there are many …

Lexicase parent selection filters the population by considering one random training case at a
time, eliminating any individual with an error for the current case that is worse than the best …

Parent selection algorithms (selection schemes) steer populations through a problem's
search space, often trading off between exploitation and exploration. Understanding how …

Lexicase selection is a selection method that was developed for parent selection in genetic
programming. In this chapter, we present a study of lexicase selection in a non-genetic …

Bloat, a well-known phenomenon in Evolutionary Computation, often slows down evolution
and complicates the task of interpreting the results. We propose Lexi2, a new selection and …

GRAPE is an implementation of Grammatical Evolution (GE) in DEAP, an Evolutionary
Computation framework in Python, which consists of the necessary classes and functions to …