Lexicase Selection in Learning Classifier Systems

Sneha Aenugu, Lee Spector

Abstract:

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 aggregated data accuracy. While
the method has demonstrated promise in genetic programming and
more recently in genetic algorithms, its applications in other forms
of evolutionary machine learning have not been explored. In this pa-
per, we investigate the use of lexicase parent selection in Learning
Classifier Systems (LCS) and study its effect on classification prob-
lems in a supervised setting. We further introduce a new variant
of lexicase selection, called batch-lexicase selection, which allows
for the tuning of selection pressure. We compare the two lexicase
selection methods with tournament and fitness proportionate se-
lection methods on binary classification problems. We show that
batch-lexicase selection results in the creation of more generic rules
which is favorable for generalization on future data. We further
show that batch-lexicase selection results in better generalization
in situations of partial or missing data.