Microgenetic optimization algorithm for optimal wavefront shaping

Abstract

One of the main limitations of utilizing optimal wavefront shaping in imaging and authentication applications is the slow speed of the optimization algorithms currently being used. To address this problem we develop a micro-genetic optimization algorithm ($\mu$GA) for optimal wavefront shaping. We test the abilities of the $\mu$GA and make comparisons to previous algorithms (iterative and simple-genetic) by using each algorithm to optimize transmission through an opaque medium. From our experiments we find that the $\mu$GA is faster than both the iterative and simple-genetic algorithms and that both genetic algorithms are more resistant to noise and sample decoherence than the iterative algorithm.

13 Pages, 6 Figures

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Cited by in corpus (4)

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• Effect of experimental parameters on optimal reflection of light from opaque media
• Stability of optimal-wave-front-sample coupling under sample translation and rotation