A Hyper-feasible Solutions Based Update Weight Vectors Evolutionary Algorithm for Constrained Multiobjective Optimization Problem
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Bibliographic record
Abstract
Existing decomposition-based constrained multi-objective evolutionary algorithms (CMOEAs) use fixed decomposition methods to partition the search space, which may limit their ability in solving some certain constrained multi-objective optimization problems (CMOPs). To address this issue, this paper introduces the concept of hyper-feasible solutions, which are extracted from feasible solutions and promising infeasible solutions. Based on these solutions, we propose a novel algorithm called HSWU, which adaptively partitions the search space and guides the search direction to enhance the efficiency of solution searching. Experimental results on three benchmark test suites demonstrate that HSWU outperforms five state-of-the-art CMOEAs in terms of performance.
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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 | 0.001 | 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