Abstract
Lexicase selection is a selection method for evolutionary computation in which individuals are selected by filtering the population according to performance on test cases, considered in random order. When used as the parent selection method in genetic programming, lexicase selection has been shown to provide significant improvements in problem-solving power. In this chapter we investigate the reasons for the success of lexicase selection, focusing on measures of population diversity. We present data from eight program synthesis problems and compare lexicase selection to tournament selection and selection based on implicit fitness sharing. We conclude that lexicase selection does indeed produce more diverse populations, which helps to explain the utility of lexicase selection for program synthesis.
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