Memory Length in Hyper-heuristics: An Empirical Study
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
Hyper-heuristics are an emergent optimisation methodology which aims to give a higher level of flexibility and domain-independence than is currently possible. Hyper-heuristics are able to adapt to the different problems or problem instances by dynamically choosing between heuristics during the search. This paper is concerned with the issues of memory length on the performance of hyper-heuristics. We focus on a recently proposed simulated annealing hyper-heuristic and choose a set of hard university course timetabling problems as the test bed for this empirical study. The experimental results show that the memory length can affect the performance of hyper-heuristics and a good choice of memory length is able to improve solution quality. Finally, two dynamic approaches are investigated and one of the approaches is shown to be able to produce promising results without introducing extra sensitive algorithmic parameters.
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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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it