Comparing genetic algorithm and guided local search methods by symmetric TSP instances
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
This paper aims at comparing Genetic Algorithm (GA) and Guided Local Search (GLS) methods so as to scrutinize their behaviors. Authors apply the GLS program with the Fast Local Search (FLS), developed at University of Essex, and implement a genetic algorithm with partially-mapped and order crossovers, reciprocal and inversion mutations, and rank and tournament selections in order to experiment with various Travelling Salesman Problems. The paper then ends up with two prominent conclusions regarding the performance of these meta-heuristic techniques over wide range of symmetric-TSP instances. First, the GLS-FLS strategy on the s-TSP instances yields the most promising performance in terms of the near-optimality and the mean CPU time. Second, the GA results are comparable to GLS-FLS outcomes on the same s-TSP instances. In the other word, the GA is able to generate near optimal solutions with some compromise in the CPU time.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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