Quality Diversity Algorithm

Quality Diversity (QD) algorithms are a class of optimization methods designed to discover a diverse set of high-performing solutions, rather than a single optimal one. Current research focuses on improving the efficiency of QD algorithms, particularly in resource-constrained environments and uncertain domains, often employing models like MAP-Elites and incorporating techniques like gradient information and surrogate models to accelerate the search process. This approach is proving valuable in diverse fields such as robotics, materials science, and game design, offering solutions that are both high-performing and robust to variations in conditions or objectives. The development of standardized benchmarks and the theoretical analysis of QD algorithms are also active areas of investigation.