ABSTRACT
This chapter addresses the basic principles of the Electrical Impedance Tomography (EIT) and the image reconstruction as an optimization problem solved by the following search and optimization algorithms: genetic algorithms, simulated annealing, particle swarm optimization, and fish school search. Electrical impedance tomography (EIT) is a non-invasive set of techniques of image reconstruction, which can be used to obtain estimated images of electrical conductivity or permittivity in the inside of a section of any body/object through electrical quantity measured in its surface. The experiments realized in this work were made using the tool Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software (EIDORS), a computational tool open sourced and used in EIT. The genetic algorithm, following Fisher's formulation, uses the differing fitness of variants in the current generation to propagate improvements to the next generation, but the GA places strong emphasis on the variants produced by crossover.
