Electrical Impedance Tomography based on Genetic Algorithm
This work addresses the EIT inverse computing problem for medical or industrial imaging applications, but appears incremental as it applies an existing GA method to this domain without novel algorithmic contributions.
The paper tackles the Electrical Impedance Tomography (EIT) inverse problem by applying a Genetic Algorithm (GA) as an optimization method, exploring its combination with regularization operators like Tikhonov and Mumford-Shah, but does not report specific numerical results or performance gains.
In this paper, we applies GA algorithm into Electrical Impedance Tomography (EIT) application. We first outline the EIT problem as an optimization problem and define a target optimization function. Then we show how the GA algorithm as an alternative searching algorithm can be used for solving EIT inverse problem. In this paper, we explore evolutionary methods such as GA algorithms combined with various regularization operators to solve EIT inverse computing problem. Key words: Electrical Impedance Tomography (EIT), GA, Tikhonov operator , Mumford-Shah operator, Particle Swarm Optimization(PSO), Back Propagation(BP).