Refine
H-BRS Bibliography
- yes (3)
Departments, institutes and facilities
Document Type
- Conference Object (2)
- Preprint (1)
Language
- English (3)
Has Fulltext
- no (3)
Keywords
- Compositional Pattern Producing Networks (3) (remove)
A novel approach to produce 2D designs by adapting the HyperNEAT algorithm to evolve non-uniform rational basis splines (NURBS) is presented. This representation is proposed as an alternative to previous pixel-based approaches primarily motivated by aesthetic interests, and not designed for optimization tasks. This spline representation outperforms previous pixel-based approaches on target matching tasks, performing well even in matching irregular target shapes. In addition to improved evolvability in the face of a well defined fitness metric, a NURBS representation has the added virtues of being continuous rather than discrete, as well as being intuitive and easily modified by graphic and industrial designers.
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.
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.