A Good Number of Forms Fairly Beautiful: An Exploration of Biologically-Inspired Automated Design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Artificial Embryogeny (AE) can be described as the use of a dynamical system as a mid-step in a design process; Through emulating Biological Embryogenesis, we hope to reach levels of complexity and robustness currently impossible. AE is a new field, and suffers from a lack of standards and meaningful means of evaluation. In this document, we review existing work, discussing motivations and merits of existing approaches. Throughout, we argue that a viewpoint which does not regard environment as a primary source of information risks taking a naive view of evolution. We argue that ``complexity'' is vaguely and inconsistently defined, and propose several novel measures; Perhaps the simplest model of AE, the Terminating Cellular Automaton, is introduced, and used to compute and contrast our measures. Next, the Deva family of AE algorithms is introduced, a modular Cellular Automaton-like group. A domain of application from Civil Engineering is chosen as an interpretation of the grown organisms. It is initially shown that it is possible to use a Deva algorithm to evolve Plane Trusses successfully, this interpretation providing a discipline-independent measure of success. A series of empirical experiments is undertaken, showing the relative efficacy and effects of several model-level strategies in the context of the evolution of structural design. Finally, we explore the role of environment as a constraint on development of structural form. We demonstrate a strong resistance to environmental change by successfully re-growing the organisms in new environments, showing that some Deva organisms are adding information from the environment to their overall morphology; This provides an arti?cial analogue to the re-use of genes which characterizes biological development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it