Accelerating GP Genome Evaluation Through Real Compilation with a Multiple Program Single Data Approach
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
Genetic Programming (GP) presents a unique challenge in fitness evaluation due to the need to repeatedly execute the evolved programs, often represented as tree structures, to assess their quality on multiple input data. Traditional approaches rely on interpreting these program trees, which can be computationally expensive. This paper proposes an optimization method that leverages code generation using a novel strategy and Just-In-Time (JIT) compilation to significantly improve the efficiency of fitness evaluation in GP. We propose to revisit using an actual compiler to transform a GP individual into native computer code executable quickly on the CPU. Our GP implementation is a simple tree-based approach that makes it easy for researchers to experiment with, while the evaluation function shows high performance. Preliminary results in Symbolic Regression on artificial datasets demonstrate that our approach is more than 6× faster than a popular GP framework that uses high-performance single-function evaluation but in a stack-based interpreter mode.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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