The representation, or encoding, utilized in evolutionary algorithms has a substantial effect on their performance. Examination of the suitability of widely used representations for quality diversity optimization (QD) in robotic domains has yielded inconsistent results regarding the most appropriate encoding method. Given the domain-dependent nature of QD, additional evidence from other domains is necessary. This study compares the impact of several representations, including direct encoding, a dictionary-based representation, parametric encoding, compositional pattern producing networks, and cellular automata, on the generation of voxelized meshes in an architecture setting. The results reveal that some indirect encodings outperform direct encodings and can generate more diverse solution sets, especially when considering full phenotypic diversity. The paper introduces a multi-encoding QD approach that incorporates all evaluated representations in the same archive. Species of encodings compete on the basis of phenotypic features, leading to an approach that demonstrates similar performance to the best single-encoding QD approach. This is noteworthy, as it does not always require the contribution of the best-performing single encoding.

翻译：表示方法（即编码方式）对进化算法的性能具有显著影响。在机器人领域中，针对广泛使用的表示方法对质量多样性优化的适用性研究，关于最合适的编码方法得出的结论并不一致。鉴于质量多样性优化依赖具体领域特性，需要来自其他领域的更多证据。本研究比较了多种表示方法（包括直接编码、基于字典的编码、参数化编码、组合式模式生成网络和元胞自动机）对建筑领域中体素化网格生成的影响。结果表明，某些间接编码方法优于直接编码，尤其是在考虑完整表型多样性时，能够生成更多样化的解集。本文提出了一种多编码质量多样性优化方法，将所有评估的表示方法整合到同一存档中。各类编码基于表型特征进行竞争，最终展现出与最优单编码质量多样性优化方法相近的性能。值得关注的是，这种方法并不总是需要贡献性能最佳的单一编码。