Citation
Abstract
This report presents developments to a genetic algorithm that evolves antenna designs from primitive shapes. The gain patterns of individuals are evaluated using XFdtd electromagnetic simulation software and fitness is evaluated by integrating in a novel Monte Carlo simulation software assessing the performance of the designs in a science environment. This work is an update to a prior report demonstrating evolution of dipole-like antennas with a fitness focused on convergences to similar gain patterns or features in antenna performance. In this report we show the algorithm is capable of designing dipole-like antennas optimized for specific performance metrics based on simulation software for science experiments. The algorithm was updated to incorporate an age-layered population structure to improve convergence and computational efficiency. This report presents initial results on an improved antenna design for an in-ice neutrino detector using the simulation software of the Askaryan Radio Array (ARA) experiment as a proof-of-concept. Several additional design features allowing for reflectors and multi-feed are currently in the testing phase and will also be briefly discussed.
Keywords
Details
- Volume
- 42-242
- Published
- August 15, 2025
- Pages
- 1–29
- File Size
- 3.2 MB