A genetic algorithm for optimization of logical topologies in optical networks
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Bibliographic record
Abstract
In this paper, we introduce a new method, based on genetic algorithms (GA), for optimizing the logical topology of multi-hop optical networks. Our aim is to combine the advantages of the mathematical optimization based approach with the advantages of using regular topologies. We propose implementing the logical topology as a pre-selected regular topology, so that we can exploit the attractive properties of regular graphs. The problem of topology design is thus reduced to that of finding an appropriate mapping between the physical and logical nodes. This mapping is accomplished using a genetic algorithm, which attempts to minimize the congestion of the network, based on the given traffic matrix. We propose a number of new crossover strategies designed to preserve efficient subgraphs within a network. We evaluate our approach by comparing the results with traditional hill-climbing techniques as well as genetic algorithms using standard crossover operators found in the literature. The results indicate our GA, based on the new crossover operators, consistently provide significant improvements over these methods.
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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 | 0.000 | 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