Open AccessEvolving expertise for automated lens optimization
Author(s) -
Caleb Gan,
Rongguang Liang
Publication year - 2020
Publication title -
applied optics
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 0.668
H-Index - 197
eISSN - 2155-3165
pISSN - 1559-128X
DOI - 10.1364/ao.391888
Subject(s) - computer science , maxima and minima , encoding (memory) , process (computing) , image warping , lens (geology) , optimization problem , generality , genetic algorithm , artificial intelligence , optics , algorithm , mathematics , psychology , mathematical analysis , physics , machine learning , psychotherapist , operating system
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.