When using Quality Diversity (QD) optimization to solve hard exploration or deceptive search problems, we assume that diversity is extrinsically valuable. This means that diversity is important to help us reach an objective, but is not an objective in itself. Often, in these domains, practitioners benchmark their QD algorithms against single objective optimization frameworks. In this paper, we argue that the correct comparison should be made to \emph{multi-objective} optimization frameworks. This is because single objective optimization frameworks rely on the aggregation of sub-objectives, which could result in decreased information that is crucial for maintaining diverse populations automatically. In order to facilitate a fair comparison between quality diversity and multi-objective optimization, we present a method that utilizes dimensionality reduction to automatically determine a set of behavioral descriptors for an individual, as well as a set of objectives for an individual to solve. Using the former, one can generate solutions using standard quality diversity optimization techniques, and using the latter, one can generate solutions using standard multi-objective optimization techniques. This allows for a level comparison between these two classes of algorithms, without requiring domain and algorithm specific modifications to facilitate a comparison.

翻译：在利用质量多样性（QD）优化解决困难探索或欺骗性搜索问题时，我们假设多样性具有外在价值。这意味着多样性虽有助于达成目标，但其本身并非目标。在此类问题中，研究者常将QD算法与单目标优化框架进行基准比较。本文认为，正确的比较对象应是**多目标**优化框架。这是因为单目标优化框架依赖子目标的聚合，可能损失对自动维持种群多样性至关重要的信息。为促进质量多样性与多目标优化的公平比较，我们提出一种利用降维技术自动确定个体行为描述符及待求解目标集的方法。前者可通过标准质量多样性优化技术生成解，后者则可通过标准多目标优化技术生成解。该方法无需针对特定领域或算法进行修改，即可实现两类算法在同等条件下的比较。