Maximising the Performance of Quality-Diversity Algorithms within PCG Systems

质量多样性（Quality-Diversity，QD）算法是高性能搜索算法的一个新的分支，其优点使其与视频游戏过程内容生成（Procedural Content Generation，PCG）特别相关。然而，在撰写本文时，还没有研究对比和评估PCG域中替代QD算法的性能，这是本项目旨在解决的一个差距。通过比较实验，我的目标是发现哪种QD算法最能生成多样化和高质量的游戏内容，以及哪些因素对优化其性能最重要。该项目将首先专注于生成超级马里奥兄弟级别的领域，然后扩展到多个不同的游戏PCG空间。这项工作的成功完成将大大加深对这一领域的理解，允许使用QD算法创建更强大和更复杂的内容生成工具。

Quality-Diversity (QD) algorithms are a new branch of high performing search algorithm, the benefits of which make them especially relevant to video-game Procedural Content Generation (PCG). However, at time of writing there have been no studies contrasting and evaluating the performances of alternative QD algorithms within a PCG domain, a gap this project aims to address. Through comparative experimentation I aim to discover which QD algorithm is best able to generate diverse and high quality game content, and which factors are most important for optimising its performance. The project would first focus on the domain of generating Super Mario Bros levels and then extend to multiple different game PCG spaces. Successful completion of this work will substantially deepen understanding of this area, allowing the creation of more powerful and sophisticated content generation tools using QD algorithms.