Genetic algorithms (GAs) are a type of computational optimization algorithms that emulate
natural selection to “evolve" candidate solutions to a given problem. The GENETIS collaboration
applies GAs to experimental design to efficiently optimize for improved performance. To this
end, GENETIS has begun by designing GAs for the evolution of vertically polarized (VPol)
antennas used in ultra-high energy (UHE) neutrino observatories. Due to the low flux of UHE
neutrinos, as well as their small cross sections, it is essential to maximize the sensitivity of
neutrino observatories at every step of the experiment. Neutrino observatories make use of
radio signals produced by UHE neutrino interactions by observing large volumes of ice. The
Askaryan Radio Array (ARA) achieves this by distributing stations of antennas across vast areas
near the South Pole. Experiments like ARA measure their expected performance using simulation
software that incorporates properties of the experiment and the physics of neutrino interactions in
ice. The Physical Antenna Evolutionary Algorithm (PAEA) evolves the geometric properties of
antennas within the physical constraints of specific UHE neutrino experiments and simulates their
responses using EM simulation software XFdtd. To measure the performance of antenna designs,
PAEA uses neutrino observatories’ simulation software with the evolved antennas’ responses
included. This proceeding will discuss GENETIS’ evolution of VPol antenna designs for ARA
and upcoming work on evolving more antenna designs for ARA and the Payload for Ultrahigh
Energy Observations (PUEO). New efforts to capitalize on the birefringent properties of Antarctic
ice to evolve experimental design will also be discussed.