Abstract
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 perform a 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- and four-mirror free-form systems.
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