  Lexicase selection and novelty search, two parent selection methods used in
evolutionary computation, emphasize exploring widely in the search space more
than traditional methods such as tournament selection. However, lexicase
selection is not explicitly driven to select for novelty in the population, and
novelty search suffers from lack of direction toward a goal, especially in
unconstrained, highly-dimensional spaces. We combine the strengths of lexicase
selection and novelty search by creating a novelty score for each test case,
and adding those novelty scores to the normal error values used in lexicase
selection. We use this new novelty-lexicase selection to solve automatic
program synthesis problems, and find it significantly outperforms both novelty
search and lexicase selection. Additionally, we find that novelty search has
very little success in the problem domain of program synthesis. We explore the
effects of each of these methods on population diversity and long-term problem
solving performance, and give evidence to support the hypothesis that
novelty-lexicase selection resists converging to local optima better than
lexicase selection.
