Self-Adaptive Discrete Firefly Algorithm for Minimal Perturbation in Dynamic Constraint Satisfaction Problems
Bibliographic record
Abstract
Many real-world problems such as scheduling, planning and resource allocation can be represented and solved as Constraint Satisfaction Problems (CSPs). The main challenge when tackling these applications is the fact that they occur in an evolving environment. That is, constraints might change over time and this can affect the feasibility of the solution found so far. These changes can be captured with the Dynamic CSP formalism that has been proposed and investigated in the literature. More formally, a Dynamic CSP corresponds to a series of static CSPs, each resulting from a change in the previous one as a result of the evolving world. This change corresponds to either a constraint addition or retraction. In this paper, the focus is on constraint addition (also called constraint restriction) and the goal is to search for the most similar solution satisfying the old constraints and the new ones. In this regard, we propose a new method based on the Firefly algorithm for solving this particular problem with minimal perturbation. To assess the efficiency of new technique, we conducted several experiments on randomly generated dynamic CSP instances. The results achieved clearly demonstrate the efficiency of our algorithm, over other known exact and approximation techniques used in the literature for solving these problems.
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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 | 0.001 | 0.001 |
| 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 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".