Depictions of genotypic space for evaluating the suitability of different recombination operators
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
When the genetic algorithm recombines two parent genotypes, the differences between them define a genotypic subspace, and any offspring produced should be confined to this subspace. Although this might seem insignificant, those recombination (or crossover) operators that violate this principle can direct a search away from the region (in genotypic space) that contains the two parent genotypes. This is contrary to the task for which the recombination operator was originally developed and can be detrimental, so this paper introduces a visualization that can be used to detect violations of this principle. The methodology also inspired the development of a different approach to recombining permutations, and a brief case study shows that an alternative recombination operator that does not violate this principle can be used to achieve a performance improvement over previous attempts to optimize Field-Programmable Gate-Array placements using a genetic algorithm. We believe that this technique will be invaluable for developing additional recombination operators.
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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 | 0.001 | 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