Genetic Algorithm Optimization for Automatic Scheduling in the System at State Junior High School Four Binjai
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
Binjai State Junior High School faces challenges in developing an optimal class schedule each semester. The manual process currently in place is often time-consuming, error-prone, and inflexible in adapting to sudden changes such as teacher changes or school policy changes. Furthermore, scheduling conflicts frequently occur, where a teacher is scheduled to teach two different classes at the same time, or a class has two subjects in one session. This scheduling process must consider various constraints, such as teacher availability, class size, subject matter, and limited learning space. The manual scheduling process is often time-consuming, error-prone, and difficult to adapt to sudden changes such as teacher changes or curriculum changes. To address these challenges, an automated system is needed that can generate schedules efficiently and optimally. Genetic Algorithms are a method in artificial intelligence that can solve optimization problems by mimicking biological evolutionary mechanisms such as selection, crossover, and mutation. By implementing Genetic Algorithms in the scheduling system, it is hoped that more optimal schedules can be produced by reducing schedule conflicts and increasing time efficiency in the scheduling process.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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