We present a process to locate the desired local optimum of high-dimensional design problems such as the optimization of freeform mirror systems. By encoding active design variables into a binary vector imitating DNA sequences,we are able to performa genetic optimization of the optimization process itself. The end result is an optimization route that is effectively able to sidestep local minima by warping the variable space around them in a way that mimics the expertise of veteran designers. The generality of the approach is validated through the automated generation of high-performance designs for off-axis three- A nd four-mirror free-form systems.
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics
- Engineering (miscellaneous)
- Electrical and Electronic Engineering