A Constrained Multi-objective Co-Evolutionary Algorithm Based on Operator Score and Reward
Why this work is in the frame
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Bibliographic record
Abstract
This kind of algorithm composed of multiple operators, when solving different constrained multi-objective optimization problems (CMOPs), always has operators with good effects guiding the population to seek a better Pareto Front (PF). However, in the evolutionary process of such algorithms, there exist operators that have no effect but still generate offspring, thereby slowing down the convergence speed of the algorithm. To accelerate the convergence speed of the algorithm, a constrained multi-objective co-Evolutionary algorithm based on operator score and reward (SRCA) is presented in this paper, this SRCA algorithm has proposed an operator evaluation and operator reward mechanism which attempt to select operators that are beneficial to the convergence and diversity of the population for reproduction. The experimental results demonstrate that SRCA algorithm can effectively expedite the convergence speed and enhance the diversity of the population.
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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 | 0.000 | 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