Focus Group: An Optimization Algorithm Inspired by Human Behavior
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 presents a novel optimization algorithm, namely focus group (FG) algorithm, for solving optimization problems. The proposed algorithm is inspired by the behavior of group members to share their ideas (solutions) about a specific subject and trying to improve the solutions based on the cooperation and discussion. In the proposed algorithm, all the members present their solutions about the subject and all the suggested solutions proportional to their fitness, leave impact on the other solutions and incline them towards themselves. While trying to improve the quality of the candidate solutions, they converge to the optimal solution. To improve the performance of the proposed algorithm, two genetic operators are incorporated into the algorithm. The proposed algorithm is evaluated using several constrained and unconstrained benchmark functions commonly used in the area of optimization. Experimental results, in comparison with different well-known evolutionary techniques, confirm the high performance of the proposed algorithm in dealing with the optimization problems.
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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 | 0.001 |
| Open science | 0.001 | 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