# Comparing and Combining Lexicase Selection and Novelty Search

## TL;DR

This paper introduces a novel selection method combining lexicase selection and novelty search, demonstrating improved performance in program synthesis by maintaining diversity and avoiding local optima.

## Contribution

The paper proposes a new hybrid selection method called novelty-lexicase selection, integrating novelty scores into lexicase selection to enhance exploration and problem-solving in evolutionary algorithms.

## Key findings

- Novelty-lexicase outperforms both lexicase and novelty search in program synthesis.
- Novelty search alone has limited success in the domain.
- The hybrid method better maintains diversity and resists local optima.

## Abstract

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.