GA optimization of terminal antennas by the estimation of the population density of probability using dependency trees
Why this work is in the frame
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Bibliographic record
Abstract
The paper presents an application of a GA algorithm to the optimization of terminal antennas, where the number of variables to optimize and the complexity of the problem could make standard GA approaches fail. This GA algorithm is based on the estimation of the density of probability of the highest fitness chromosomes in the population. This estimation is achieved by the use of dependency trees whose structure varies dynamically along the optimization process. A general overview of Bayesian networks and probability theory is presented. The algorithm based on dependency trees (TREE) is presented with some examples and compared to standard GA with dual population (DUAL) and with linkage crossover operator based GA (GLINX). The structure optimised is an antenna covering three frequency bands (GSM, DCS and UMTS), with one feed port for the two lower bands and another for the upper band. Convergence curves are presented for the three algorithms.
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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