Genetic Programming Based Surrogate Modeling for Synthesis of Printed Ridge Gap Waveguide Unit Cells

This paper introduces, for the first time, a scalable and interpretable genetic programming (GP)-based surrogate modeling approach within an explainable AI framework for the synthesis of Printed Ridge Gap Waveguide (PRGW) unit cells. The proposed approach targets stop-band frequencies ranging from 3 to 300 GHz. The dataset is initially generated using full-wave electromagnetic simulations and augmented using a conditional generative adversarial network (cGAN) to enrich the feature space and scale the dataset to approximately one million samples. The cGAN is used exclusively during data preparation. Leveraging the augmented dataset, a GP-based surrogate model is constructed and expressed as a closed-form analytical relationship that maps the desired stop-band frequency and substrate material properties to the corresponding PRGW unit-cell dimensions. The proposed method improves reliability, significantly reduces computational time, and demonstrates superior performance compared with conventional trial-and-error procedures and traditional machine learning techniques in terms of mean squared error (MSE), mean absolute error (MAE), and efficiency. Experimental validation is performed through the design, fabrication, and measurement of two PRGW-based waveguides targeting Internet of Space (12-16 GHz) and mid-band 5G (3-4 GHz) applications. The measured results confirm the effectiveness of the proposed GP-based surrogate modeling framework for automated RF and microwave component and subsystem design.