Applying Opposition-Based Ideas to the Ant Colony System
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents several extensions to an algorithm in the family of ant colony optimization, the ant colony system. The proposed extensions are based on the idea of opposition and attempt to increase the exploration efficiency of the solution space. The modifications focus on the solution construction phase of the ant colony system. Three of the proposed methods work by pairing the ants and synchronizing their path selection. The two other approaches modify the decisions of the ants by using an opposite-pheromone content. Results on the application of these algorithms on travelling salesman problem instances demonstrate that the concept of opposition is not easily applied to the ant algorithm. Only one of the pheromone-based methods showed performance improvements that were statistically significant. The quality of the solutions increased and more optimal solutions were found. The other extensions showed no clear improvement. Further work must be conducted to explore the successful pheromone-based approach, as well as to determine if opposition should be applied to a different phase of the algorithm
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 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