Automatic modeling of a novel gene expression programming based on statistical analysis and critical velocity
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 basic principle of GEP is briefly introduced. And considering the defects of classic GEP such as lack of variety, the problem of convergence and blind searching without learning mechanism, a novel GEP based on statistical analysis and stagnancy velocity is proposed (called AMACGEP). It mainly has the following characteristics: First, improve the initial population by statistic analysis of repeated bodies. Second, introduce the concept of stagnancy velocity to adjust the searching space, evolution velocity, the diversity of individuals and the accuracy of prediction. Third, introduce dynamic mutation operator to improve the diversity of individuals and the velocity of convergence. Compared with other methods like traditional methods, methods of neural network, classic GEP and other improved GEPs in automatic modeling of complex function, the simulation results show that the AMACGEP set up by this paper is better.
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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