Evaluation of The Contract Or-Patch Heuristic Eor The Asymmetric Tsp<sup>1</sup>
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Bibliographic record
Abstract
In this paper we further investigate tour construction algorithms for the Asymmetric Traveling Salesman Problem (ATSP). In [14] we introduced a new algorithm, called Contract-or-Patch (COP). We have tested the algorithm together with other well-known and new heuristics on a variety of families of ATSP instances. In our study, COP has demonstrated good performance, clearly outperforming all other algorithms on robustness. It has either produced the shortest tours or came close to the leader on eaeb of the seven families tested,while each of the remaining algorithms failed on at least two families of instances.In this paper we introduce three new variants of Che COP algorithm, and perform an extensive computational study of the original as well as new versions of the algorithm on a variety of ATSP instances. We also study the influence of the threshold parameter on the quality of tours produced by COP. We conclude the study by recommending one of the new versions of COP as a replacement for the original algorithm. The modified algorithm produces higher-quality tours iban the original version, and has a nice property of being a mucb simpler algorithm. We also recommend a good universal choice of the parameter value.
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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 | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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