  In many evolutionary computation systems, parent selection methods can
affect, among other things, convergence to a solution. In this paper, we
present a study comparing the role of two commonly used parent selection
methods in evolving machine learning pipelines in an automated machine learning
system called Tree-based Pipeline Optimization Tool (TPOT). Specifically, we
demonstrate, using experiments on multiple datasets, that lexicase selection
leads to significantly faster convergence as compared to NSGA-II in TPOT. We
also compare the exploration of parts of the search space by these selection
methods using a trie data structure that contains information about the
pipelines explored in a particular run.
