A Pruning based Ant Colony Algorithm for Minimum Vertex Cover Problem.
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
Given an undirected, unweighted graph G = (V , E) the minimum vertex cover (MVC) problem is a subset of V whose cardinality is minimum subject to the premise that the selected vertices cover all edges in the graph. In this paper, we propose a meta-heuristic based on Ant Colony Optimization (ACO) approach to find approximate solutions to the minimum vertex cover problem. By introducing a visible set based on pruning paradigm for ants, in each step of their traversal, they are not forced to consider all of the remaining vertices to select the next one for continuing the traversal, resulting very high improvement in both time and convergence rate of the algorithm. We compare our algorithm with two existing algorithms which are based on Genetic Algorithms (GAs) as well as its testing on a variety of benchmarks. Computational experiments evince that the ACO algorithm demonstrates much effectiveness and consistency for solving the minimum vertex cover problem.
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.
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