Two-Strategy reinforcement group cooperation based symbiotic evolution for TSK-type fuzzy controller design
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a TSK-type fuzzy controller (TFC) with a two-strategy reinforcement group cooperation based symbiotic evolution (TSR-GCSE) for solving various control problems. The TSR-GCSE proposes the two-strategy reinforcement (TSR) signal designed to improve the performance of the traditional reinforcement signal designed. Moreover, the TSR-GCSE is different from the traditional symbiotic evolution; with each population in the TSR-GCSE method is divided to several groups. Each group represents a set of the chromosomes that belongs to a fuzzy rule and can cooperate with other groups to generation the better chromosomes by using elites-base compensation crossover strategy (ECCS). The illustrative examples show that the proposed method has the better time steps and CPU times than other existing methods.
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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 | 0.004 | 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 | 0.001 | 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