  Evolutionary algorithms are a type of artificial intelligence that utilize
principles of evolution to efficiently determine solutions to defined problems.
These algorithms are particularly powerful at finding solutions that are too
complex to solve with traditional techniques and at improving solutions found
with simplified methods. The GENETIS collaboration is developing genetic
algorithms to design antennas that are more sensitive to ultra high energy
neutrino induced radio pulses than current detectors. Improving antenna
sensitivity is critical because UHE neutrinos are rare and require massive
detector volumes with stations dispersed over hundreds of km squared. The
GENETIS algorithm evolves antenna designs using simulated neutrino sensitivity
as a measure of fitness by integrating with XFdtd, a finite difference time
domain modeling program, and with simulations of neutrino experiments. The best
antennas will then be deployed in ice for initial testing. The genetic
algorithm's aim is to create antennas that improve on the designs used in the
existing ARA experiment by more than a factor of 2 in neutrino sensitivities.
This research could improve antenna sensitivities in future experiments and
thus accelerate the discovery of UHE neutrinos. This is the first time that
antennas have been designed using genetic algorithms with a fitness score based
on a physics outcome, which will motivate the continued use of genetic
algorithm designed instrumentation in astrophysics and beyond. This proceeding
will report on advancements to the algorithm, steps taken to improve the
genetic algorithm performance, the latest results from our evolutions, and the
manufacturing road map.
