Flow of control in linear genetic programming
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
Traditional flow of control for linear genetic programming includes structures such as if-then-else statements combined with gotos. In this study we examine additional classes of flow of control structures. The first is called the alternator. This is a deterministically variable flow of control that executes a goto every other time it is accessed. We demonstrate that evolution can use alternators that jump past one another to create solutions with significantly more complexity than those created by solutions without alternators for a simple binary string generation problem. The alternator, while clearly useful, would be difficult for human programmers to use effectively. The alternator thus demonstrates a strong disjunction between human-friendly and evolution-friendly programming languages. Domain specific flow of control structures tailored to the environment being studied are also examined. These are statements carefully designed for the problems being solved. Allowing controllers solving the Tartarus task to change the flow of control based on knowledge of their position in the interior boundary of a world substantially enhances the performance of the controllers. Comparison of the three different fitness functions used demonstrates that the benefit of the alternate flow-of-control is domain specific.
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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 | 0.000 | 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