Gradient-Boosted Decision Tree Optimizer for Antenna Optimization
Bibliographic record
Abstract
The use machine learning-assisted optimization methods in the design of antennas have been increasing. Although neural networks (NNs) and Gaussian process regression (GPR) are widely used, their scalability to higher dimensions poses several challenges, such as the requirement for excessive data, extensive hyper-parameter tuning, and longer training times. In contrast, gradient-boosted decision trees (GBDTs) exhibit superior performance with limited training data, along with faster training and more efficient hyper-parameter tuning. Motivated by these advantages, we introduce a GBDT-assisted optimization (GBDTO) algorithm tailored for high dimensional problems. Beginning with an initial sample set, GBDTO builds a GBDT model and sequentially samples the input parameter space while searching for an optimal objective value. Compared to Bayesian optimization (BO) and NN-assisted optimization (ONN), GBDTO achieves faster convergence and superior objective values, as demonstrated through benchmarks using the Black-Box Optimization Benchmarking test suite, and several antenna designs. Numerical experiments across 480 instances of 12-dimensional 24 functions demonstrate 13% and 31% improvement in mean rank count over BO and ONN, respectively. Moreover, high dimensional antenna design examples indicate more than 50% faster convergence for a given optimization target and 7 − 54% improvement in the objective value for a fixed number of iterations, compared to BO and ONN. In addition to its optimization efficiency, GBDTO offers inherent and efficient feature importance analysis, which is extremely useful for user guidance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".