  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 paper, we investigate the use of lexicase
parent selection in Learning Classifier Systems (LCS) and study its effect on
classification problems 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 selection 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.
