An Improved Tabu Search Method For The Weighted Constraint Satisfaction Problem
Why this work is in the frame
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Bibliographic record
Abstract
Aiming at developing a general problem solver for combinatorial optimization problems, we consider in this paper the weighted constraint satisfaction problem (WCSP), which, given a number of constraints and their weights of importance, asks to minimize the total weight of unsatisfied constraints. We propose a tabu search algorithm for WCSP with the features that it uses an evaluation function, defined in terms of the modified weights of constraints, for guiding the search, and that it incorporates an automatic control mechanism of the weights in the evaluation function. Using this code, we solved a number of problems including those from real applications such as generalized assignment, set covering, parallel shop scheduling, timetabling and nurse scheduling. Many problems that arise in cellular manufacturing can also be formulated as WCSP, including the problems of cell formation and tool selection. Our computational results indicate that the control mechanism of weights makes our tabu search more powerful, and our algorithm is practically usable.
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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 | 0.002 | 0.005 |
| 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