Learning and using hyper-heuristics for variable and value ordering in constraint satisfaction 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
This paper explores the use of hyper-heuristics for variable and value ordering in binary Constraint Satisfaction Problems (CSP). Specifically, we describe the use of a symbolic cognitive architecture, augmented with constraint based reasoning as the hyper-heuristic machine learning framework. The underlying design motivation of our approach is to do more with less. Specifically, the approach seeks to minimize the number of low level heuristics encoded yet dramatically expand the expressiveness of the hyper-heuristic by encoding the constituent measures of each heuristic, thereby providing more opportunities to achieve improved solutions. Further, the use of a symbolic cognitive architecture allows us to encode hierarchical preferences which extend the effectiveness of the hyper-heuristic across problem types. Empirical experiments are conducted to generate and test hyper-heuristics for two benchmark CSP problem types: Map Coloring; and, Job Shop Scheduling. Results suggest that the hyper-heuristic approach provides a dramatically higher level of representational granularity allowing superior intra-problem and inter-problem solutions to be secured over traditional combinations of variable and value ordering heuristics.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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