Quality Diversity Search

Quality Diversity (QD) search is an optimization technique aiming to discover a diverse set of high-performing solutions, rather than a single optimal one, across various behavioral niches. Current research focuses on improving QD algorithms like MAP-Elites and CMA-ME, particularly for dynamic environments and incorporating memory mechanisms to enhance solution quality and diversity. This approach finds applications in diverse fields, including robotics (e.g., evolving robust soft robotic gaits), creative design (e.g., generating aesthetically diverse artwork), and automated red teaming, demonstrating its broad utility in exploring complex search spaces and generating innovative solutions.