The DOI matches its Crossref record.
The advantages of generative grammatical encodings for physical design
Abstract
One of the applications of evolutionary algorithms is the automatic creation of designs. For evolutionary techniques to scale to the complexities necessary for actual engineering problems, it has been argued that generative systems, where the genotype is an algorithm for constructing the final design, should be used as the encoding. We describe a system for creating generative specifications by combining Lindenmayer systems with evolutionary algorithms, and we apply it to the problem of generating table designs. Designs evolved by our system reach an order of magnitude more parts than previous generative systems. Comparing it against a non-generative encoding, we find that the generative system produces designs with higher fitness and is faster than the non-generative system. Finally, we demonstrate the ability of our system to go from design to manufacture by constructing evolved table designs using rapid prototyping equipment.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Search-Based Procedural Content Generation: A Taxonomy and Survey
Togelius, J., Yannakakis, G. N., Stanley, K. O., & Browne, C. (2011) · IEEE Transactions on Computational Intelligence and AI in Games · doi:10.1109/tciaig.2011.2148116 · Cited by 706
Quality Diversity: A New Frontier for Evolutionary Computation
Pugh, J. K., Soros, L. B., & Stanley, K. O. (2016) · Frontiers in Robotics and AI · doi:10.3389/frobt.2016.00040 · Cited by 514
Experience-Driven Procedural Content Generation
Yannakakis, G. N., & Togelius, J. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.6 · Cited by 470
Metadata
- Authors
- Hornby, G. S., & Pollack, J. B.
- Venue
- Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546)
- Publisher
- IEEE
- Published
- 2002-11-13
- Volume
- 1
- Pages
- 600-607
- OpenAlex
- W1514436730
- Cited by
- 176
- References
- 0
- FWCI
- 14.68
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The advantages of generative grammatical encodings for physical design. https://app.cochat.ai/papers/10.1109/cec.2001.934446
- The paper
- Hornby, G. S., & Pollack, J. B. (2002). The advantages of generative grammatical encodings for physical design. Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546). https://doi.org/10.1109/cec.2001.934446
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.