Preference-Based Stepping Ahead Firefly Algorithm for Solving Real-World Uncapacitated Examination Timetabling Problem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Swarm-based intelligent optimization algorithms that employ principles of collective behavior have been gaining traction as a viable solution in optimization research. One area of optimization is the Examination Timetabling Problem (ETP), which presents a significant challenge in many Higher Education Institutes (HEIs). This paper proposes a novel approach to solving the Uncapacitated Examination Timetabling Problem (UETP) where a stepping-ahead mechanism is utilized with threshold acceptance in the Firefly Algorithm (FA). The proposed method improves exploration with the use of the stepping-ahead mechanism, while threshold acceptance allows for better exploitation of the search space. Initially, a neighborhood search mechanism is employed as the discretization of FA to improve solution quality, known as Kempe Chain-based neighborhoods. The proposed method is tested on 7 UETP problems, with the results showing comparative performance to the best solutions available in the literature for the Toronto exam timetabling dataset. The selection of seven problems is made with exams totaling less than 400, this allows to create a manageable yet representative benchmark. The study further extends the experiment to a real-world dataset collected from an HEI. The use of a real-world dataset allows us to see the potential of the algorithm and at the same time evaluate its performance under realistic conditions and resource constraints. The proposed stepping-ahead mechanism has the potential for use in other domains, such as robotics and engineering. Overall, this paper presents a new methodology for solving the UETP that has the potential to offer superior results when compared to existing approaches.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| 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