Probabilistic developmental program evolution
Why this work is in the frame
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Bibliographic record
Abstract
A Probabilistic Model Building Genetic Programming technique for automatic program synthesis is introduced. The approach, called Probabilistic Developmental Program Evolution (PDPE), draws on the Probabilistic Incremental Program Evolution (PIPE) learning algorithm, but employs the Developmental Genetic Programming representations of Gene Expression Programming (GEP). PDPE induces a population of programs, encoded as fixed-length GEP chromosomes, by iteratively refining and randomly sampling a probability distribution of program instructions stored in a vector called probability prototype chromosome (PPC). This refining, however, is accomplished solely by means of mutation of the PPC. We compared PDPE with PIPE and GEP on a function regression problem and the 6-bit parity problem. Our results show that PDPE outperforms PIPE in terms of solution quality and variance. It also outperforms GEP in terms of solution quality, but not in terms of variance.
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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