Diversity Maximization

Diversity maximization focuses on finding a set of diverse, high-quality solutions to a problem, rather than a single optimal solution. Current research explores various algorithms, including evolutionary algorithms (like $(\mu + \lambda)$ EA and variations), density-based methods (such as Density Descent Search), and approaches incorporating human feedback to define diversity metrics. This field is significant because diverse solution sets offer robustness, adaptability, and enhanced understanding of complex problems, with applications ranging from data summarization and resource allocation to reinforcement learning and robotics.