Microgenetic optimization algorithm for optimal wavefront shaping

TL;DR

This paper introduces a micro-genetic algorithm ($GA) for wavefront shaping, significantly improving optimization speed and robustness against noise compared to traditional methods, enhancing imaging and authentication applications.

Contribution

The paper presents a novel micro-genetic optimization algorithm specifically designed for wavefront shaping, demonstrating superior speed and noise resistance over existing algorithms.

Findings

GA is faster than iterative and simple-genetic algorithms.

Genetic algorithms are more resistant to noise and decoherence.

The proposed method improves wavefront optimization efficiency.

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.