AI Driven Evolutionary Optimization Framework for Patch Antenna Miniaturization
Bibliographic record
Abstract
This paper introduces a novel framework for antenna miniaturization through the integration of parametric slotted patch design, convolutional neural networks (CNNs), and genetic algorithm (GA) optimization. Addressing the critical need for compact, high-performance antennas in modern wireless systems such as IoT and portable electronics, our methodology employs vector graphics to generate geometrically complex slotted patches with substantial footprint reduction. A CNN-based surrogate model is developed to predict the electromagnetic response of candidate geometries. The GA then iteratively optimizes slot configurations to achieve target resonance frequencies while minimizing physical dimensions. Implemented for 3.2 GHz and 4 GHz prototypes, the approach demonstrates significant size reductions of 75 % (from$35 \times 25 ~\text{mm}$to$16 \times 12 ~\text{mm}$) and 65 % (from$26 \times 21 ~\text{mm}$to$16 \times 12 ~\text{mm}$), respectively, while maintaining excellent impedance matching. Comparative analysis with conventional rectangular patches confirms superior miniaturization capabilities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".