Applications of Evolutionary Algorithms for Instrument Design

Astrophysics, Earth science, and commercial sensing missions must incorporate high-performance instruments into tight mass, volume, and power budgets. This chapter discusses how evolutionary algorithms (EA) are one tool that can help meet this challenge. The first half of the chapter surveys the growing demand for AI-designed instruments and describes the benefits of EA in this field. It also discusses the challenges and potential solutions for incorporating AI and other computational techniques in instrument design, highlighting four barriers: computational cost, fitness function design, simulationsimulationintegration, and validation. The second half discusses an example of research into instrument design through EA with the Nebulous Project, which evolves antennas from simple building blocks. The Nebulous workflow creates individuals by combining primitive shapes into larger structures, storing the genes describing the geometry and relationship between primitive shapes in a tree dictionary. The early efforts of evolving to target three-dimensional (3D) shapes are described, followed by updating the algorithm to evolve into working antennas, using a fitness function based on electromagnetic simulationsimulationof individuals. In a case study for the Askaryan Radio Array, Nebulous produced a vertically polarized antenna that increases the effective volume across 200–800 MHz and outperforms the deployed bicone design in simulations. Ongoing work is expanding the primitive library, adding multiple feed support, and automating operator scheduling while paving the way for multi-objectivemulti-objectiveoptimization, surrogate modeling, and array-level evolution. Together, this chapter provides a road map by motivating the need for EAs for instrument design, demonstrating their power on a real science problem, and charting the next steps toward fully autonomous, science-driven hardware optimization across the space instrument community.