An Intelligent Modeling Method Based on Genetic Programming and Genetic Algorithm
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
This paper utilizes Genetic Programming(GP) and Genetic Algorithm(GA) to analyze experiment data. The purpose of this research is to establish a function model of the data. The core methodology of this research is using GP to get the approximate model first, and then optimizes the parameters and enhance the fitness value of the model by using GA. To validate this method, two examples are given: one is the reconstruction of permeability-strain equation of the rock in literature[1]; another example is the function search automatically of the wire cable isolator experiment data. In the process of programming of parse tree, this paper adopted a new way that different from three traditional methods, the parse tree is described by matrix of special size, more significantly, this new method makes the genetic operation of crossover and mutation intuitionstic, even the pellucid Matlab programming language could implement it.
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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