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The magnetic permeability of a ferrite is an important factor in designingdevices such as inductors, transformers, and microwave absorbing materialsamong others. Due to this, it is advisable to study the magnetic permeabilityof a ferrite as a function of frequency.  When an excitation that corresponds to a harmonic magnetic field \textbf{H}is applied to the system, this system responds with a magnetic flux density\textbf{B}; the relation between these two vectors can be expressed as\textbf{B}=$\mu(\omega)$ \textbf{H} . Where $\mu$ is the magnetic permeability.  In this paper, ferrites were considered linear, homogeneous, and isotropicmaterials. A magnetic permeability model was applied to NiZn ferrites dopedwith Yttrium.  The parameters of the model were adjusted using the Genetic Algorithm. In thecomputer science field of artificial intelligence, Genetic Algorithms andMachine Learning does rely upon nature's bounty for both inspiration nature'sand mechanisms. Genetic Algorithms are probabilistic search procedures whichgenerate solutions to optimization problems using techniques inspired bynatural evolution, such as inheritance, mutation, selection, and crossover.  For the numerical fitting usually is used a nonlinear least square method,this algorithm is based on calculus by starting from an initial set of variablevalues. This approach is mathematically elegant compared to the exhaustive orrandom searches but tends easily to get stuck in local minima. On the otherhand, random methods use some probabilistic calculations to find variable sets.They tend to be slower but have greater success at finding the global minimumregardless of the initial values of the variables

Numerical response of the magnetic permeability as a funcion of the frecuency of NiZn ferrites using Genetic Algorithm