Evaluation of The Contract Or-Patch Heuristic Eor The Asymmetric Tsp<sup>1</sup>
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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 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".