Solving Weighted Constraint Satisfaction Problems Using a new Self-Adaptive Discrete Firefly Algorithm
Bibliographic record
Abstract
A Weighted Constraint Satisfaction Problem (WCSP) is a Constraint Satisfaction Problem in which preferences between solutions are considered, meaning that some solutions are more preferred than others and the optimal solution is the one with minimum weight. Such problems are usually dealt with classical complete methods like bucket elimination techniques. However, since these problems are NP-hard the complete methods will require exponential time in addition to a memory space cost. Therefore, approximation methods such as metaheuristics are a good alternative as they are capable of tackling hard to solve combinatorial problems in a very efficient running time. In this regard, we propose a new self-adaptive discrete Firefly algorithm for solving WCSPs. While, like any other approximation algorithm, our method does not guarantee the optimality of the solution returned, the experiments we conducted on randomly generated WCSP instances, demonstrate its ability in returning the optimal solution in a very efficient running time.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".