Investigations of constructive approaches for examination timetable and 3d-strip packing
Bibliographic record
Abstract
This thesis aims at designing search methods that can produce competitive solutions and to some extent, are of higher generality than the state of the art search/optimisation systems. Attaining this aim would underpin the next generation of automated systems with the goal being to require less specialist knowledge in solving complex optimisation problems. The main challenge in this project is to develop systems of higher generality which can intelligently select, evolve or combine search methods (heuristics) to operate upon a wider range of problems and problem instances. This research follows that direction and contributes to the goal of exploring the generality boundary of this new trend of automating the design of search systems. The mam contributions in this thesis are divided into two parts. The first part investigates different approaches to combine constructive heuristics which are capable of producing good solutions for timetabling problems. Chapter 3 presents a weighted graph model for the exam timetabling problem where vertices and edges store several extra-attributes to improve the process of finding difficult exams and selecting timeslots for them. Chapter 4 investigates sequential and linear combinations of vertex-selection heuristics that have emerged from the weighted graph model. The results on the Toronto exam timetabling benchmark are compared with those obtained from other approaches in the literature. The second part of the research focuses on raising the level of generality for search methodologies by investigating the use of estimation of distribution algorithms into a proposed hyper-heuristic for several optimisation problems. Chapter 5 presents an extended framework for the best-fit strategy for the three-dimensional strip packing 2 problem. Chapter 6 proposes a hyper-heuristic based on estimation of distribution algorithms. Then we investigate the level of generality of the hyper-heuristic by applying it to different problem domains (graph colouring, exam timetabling, and 3D strip packing). Experimental evidence indicates that the hyper-heuristic can operate on a wide range of problems to produce some competitive results. We also demonstrate the capability of identifying the effectiveness of the low-level heuristics. This may facilitate the development of efficient automated search systems in future research. Finally, Chapter 7 evaluates all the results obtained and summarises promising future research directions.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".