The effects of randomly sampled training data on program evolution
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
The effects of randomly sampled training data on genetic programming performance is empirically investigated. Often the most natural, if not only, means of characterizing the target behaviour for a problem is to randomly sample training cases inherent to that problem. A natural question to raise about this strategy is, how deleterious is the randomly sampling of training data to evolution performance? Will sampling reduce the evolutionary search to hill climbing? Can resampling during the run be advantageous? We address these questions by undertaking a suite of different GP experiments. Parameters include various sampling strategies (single, re-sampling, ideal samples), generational and steady-state evolution, and non-evolutionary strategies such as hill climbing and random search. The experiments confirm that random sampling effectively characterizes stochastic domains during genetic programming, provided that a sufficiently representative sample is used. An u...
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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