Hybridising plant propagation and local search for uncapacitated exam scheduling problems
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
The uncapacitated exam scheduling problem (UESP) is a well-known computationally intractable combinatorial optimisation problem. It aims at assigning exams to a predefined number of periods, avoiding conflicts over the same period, and spreading exams as evenly as possible. Here, we suggest a new hybrid algorithm combining the plant propagation algorithm (PPA) and local search (LS) for it. PPA is a population-based metaheuristic that mimics the way plants propagate. To the best of our knowledge, this is the first time this idea is exploited in the context of UESP. Extensive testing on the University of Toronto benchmark dataset, and comparison against a large number of new as well as well-established methods shows that this new metaheuristic is competitive and represents a substantial addition to the arsenal of tools for solving the problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 | 0.001 |
| 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