MAP Elite

MAP-Elites is a quality-diversity optimization algorithm that aims to discover a diverse set of high-performing solutions, rather than a single optimal solution, for a given problem. Current research focuses on extending its capabilities to handle continuous multi-task optimization, high-dimensional design spaces (e.g., in soft robotics and procedural content generation), and uncertain environments, often incorporating techniques from reinforcement learning and gradient-based optimization to improve efficiency. This approach has significant implications for various fields, including robotics design, game AI, and human-AI collaborative design, by providing a richer set of design options and facilitating exploration of complex search spaces.