Abstract
We characterize the performance of standard and micro genetic algorithms when applied to focusing inside of an opaque media using feedback from a fluorescent guidestar particle. Using this feedback modality we find that the algorithms optimize more quickly (but to a lower enhancement) than when using reflective feedback, with the underlying mechanism being the fluorescence signal’s multimodal nature and lower signal-to-noise ratio. We also find that both algorithms’ performance decrease at very large numbers of bins due to decoherence effects related to a bin-dependent iteration time. To mitigate this effect we implement multithreaded functionality in the genetic algorithms and find that for our specific computer we obtain a 3.3 × improvement in speed utilizing multithreading. These results demonstrate the usefulness of both algorithms for focusing inside of opaque media, which has applications in biological imaging and the study of subsurface chemical reactions in heterogeneous materials.
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