Jones Matrix Characterization of Optical Elements via Evolutionary Algorithms

TL;DR

This paper introduces an evolutionary algorithm-based method to efficiently characterize the Jones matrix of optical elements, significantly reducing measurement requirements compared to traditional sampling methods.

Contribution

It presents a novel application of evolutionary algorithms to improve the efficiency of Jones matrix characterization in optical systems.

Findings

EA and GA outperformed general sampling with fewer measurements

Both algorithms achieved perfect convergence rate

Significant reduction in measurement effort compared to traditional methods

Abstract

Jones calculus provides a robust and straightforward method to characterize polarized light and polarizing optical systems using two-element vectors (Jones vectors) and $2 \times 2$ matrices (Jones matrices). Jones matrices are used to determine the retardance and diattenuation introduced by an optical element or a sequence of elements. Moreover, they are the tool of choice to study optical geometric phases. However, the current sampling method for characterizing the Jones matrix of an optical element is inefficient, since the search space of the problem is in the realm of the real numbers and so applying a general sampling method is time-consuming. In this study, we present an initial approach for solving the problem of finding the eigenvectors that characterize the Jones matrix of a homogeneous optical element through Evolutionary Algorithms (EAs). We evaluate the analytical performance of an EA with a Polynomial Mutation operator and a Genetic Algorithm (GA) with a Simulated Binary crossover operator and a Polynomial Mutation operator, and compare the results with those obtained through a general sampling method. The results show that both the EA and the GA out-performed a general sampling method of 6,000 measurements, by requiring in average 103 and 188 fitness functions measurements respectively, while having a perfect rate of convergence.