A hybrid algorithm for fast optimization-based synthesis of coupling matrices for advanced microwaves filterings specifications
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Bibliographic record
Abstract
This paper presents a new hybrid algorithm for the optimization-based synthesis of coupling matrices for microwave filter design, and realizing advanced specifications. The proposed algorithm receives as input the coefficients of the polynomials of the filter specification to be realized, and provides as output the corresponding coupling matrix in a few seconds. The algorithm combines a modified binary-coded genetic algorithm (GA) as the global optimizer, and an analytical algorithm based on the adaptive learning coefficient gradient descent method as the local optimizer. Unlike a traditional GAs, the GA presented in this paper uses an initial population selector and a novel mutation and selection operator. In addition, the mutation operator precedes the crossover operator in the flowchart of its GA component, thus avoiding premature homogenization of the population and preventing the global optimizer from getting stuck in a valley of local optimums for too long. This configuration improves the convergence speed of the hybrid algorithm and enables access to advanced topologies. The algorithm has been successfully used to synthesize two coupling matrices that realize complex filtering specifications.
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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)
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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