Bibliographic record
Abstract
Ce papier traite les problemes de satisfaction maximale des contraintes (Max-CSP) connus pour leur aspect NP-Complet, et ce, par une approche Multiagent (MA) des algorithmes genetiques (AGs) qui sont qualifies de couteux en termes de temps. L'objectif est donc double: d'une part, tirer profit de l'efficacite des AGs pour donner une bonne qualite aux Max-CSPs et, d'autre part, beneficier des fondements MA afin de reduire la complexite temporelle des AGs. Les agents, crees dynamiquement, cooperent pour satisfaire le maximum de contraintes. Chaque agent s'occupe d'une sous-population de chromosomes violant le meme nombre de contraintes, et ce, a l'aide d'un AG guide par le concept de template et l'heuristique de minimisation de conflits. Pour montrer l'avantage de cette approche des comparaisons experimentales avec une version centralisee sont presentees.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".