TY - CHAP U1 - Konferenzveröffentlichung A1 - Scarton, Ludovico A1 - Hagg, Alexander T1 - On the Suitability of Representations for Quality Diversity Optimization of Shapes T2 - GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference, Lisbon, Portugal, July 15-19, 2023 N2 - 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. KW - Compositional Pattern Producing Networks KW - encoding, representation KW - Quality diversity KW - parametric KW - cellular automata SN - 979-8-4007-0119-1 SB - 979-8-4007-0119-1 U6 - https://doi.org/10.1145/3583131.3590381 DO - https://doi.org/10.1145/3583131.3590381 SP - 963 EP - 971 S1 - 9 PB - Association for Computing Machinery CY - New York, NY, USA ER -