A new biologically inspired optimization algorithm
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a new biologically inspired algorithm for optimization. The algorithm, called the Paddy Field Algorithm (PFA) operates by initially scattering seeds at random in the parameter space. The number of seeds of each plant depend on the function value such that a plant closer to the optimum solution produces the most seeds. Out of these, depending on the number of neighbors of the plant, only a percentage will become viable due to pollination. In order to prevent getting stuck in local minima, the seeds of each plant are dispersed. This algorithm is tested on four sample functions along side other conventional algorithms. The effect of various parameters on the performance of the algorithm is also investigated. Its performance is also tested with a hybrid algorithm. The results show that the algorithm performs well.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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