Genetic swarm grammar programming: Ecological breeding like a gardener
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
We recently introduced swarm grammars as an extension of Lindenmayer systems to model dynamic growth processes in 3D space through a large number of interacting (swarm) agents. Grammatical rewrite rules define different types of agents and their evolution over time. Sets of parameters determine specific interaction behaviors among the generated swarms. As we will show, swarm grammars lend themselves to creating an ecology of interacting entities and dynamic structures that are built by a multitude of agents. In addition to a rather traditional approach of evolving swarm grammars through interactive genetic programming, we explore new ways of designing ecologies of swarm agents by immersing the breeder into the growth and evolution processes. The system designer takes on the role of a 'tinkerer' or 'gardener', who is equipped with tools to influence and shape the on-going growth, evolutionary, and other dynamic processes within the swarm grammar ecology. Spatial genetic operators can be directed to specific locations within the evolving swarms. This enables the breeder to overview large numbers of phenotypic developmental processes and implicitly direct their evolution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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