Generalized benchmark generation for dynamic combinatorial problems
Bibliographic record
Abstract
Several general purpose benchmark generators are now available in the literature. They are convenient tools in dynamic continuous optimization as they can produce test instances with controllable features. Yet, a parallel work in dynamic discrete optimization still lacks.In constructing benchmarks for dynamic combinatorial problems, two issues should be addressed: first, test cases that can effectively test an algorithm ability to adapt can be difficult to create; second, it might be necessary to optimize several instances of an NP-hard problem. Hence, this paper proposes a method for generating benchmarks with known solutions without the need to re-optimize. Consequently, the method does not suffer the usual limitations on the problem size or the sequence length.The paper also proposes a general framework for the generation of test problems. It aims to unify existing approaches and to form a basis for designing newer benchmarks. Such a framework can be more appreciated knowing that combinatorial problems tend to assume very distinct structures, and hence, relevant benchmarks are basically too specific to be of interest to the general reader.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".