Evolving expertise for automated lens optimization | Zendy

Caleb Gan | Zendy; Rongguang Liang | Zendy

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Evolving 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.

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