  The magnetic permeability of a ferrite is an important factor in designing
devices such as inductors, transformers, and microwave absorbing materials
among others. Due to this, it is advisable to study the magnetic permeability
of 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 isotropic
materials. A magnetic permeability model was applied to NiZn ferrites doped
with Yttrium.
  The parameters of the model were adjusted using the Genetic Algorithm. In the
computer science field of artificial intelligence, Genetic Algorithms and
Machine Learning does rely upon nature's bounty for both inspiration nature's
and mechanisms. Genetic Algorithms are probabilistic search procedures which
generate solutions to optimization problems using techniques inspired by
natural 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 variable
values. This approach is mathematically elegant compared to the exhaustive or
random searches but tends easily to get stuck in local minima. On the other
hand, random methods use some probabilistic calculations to find variable sets.
They tend to be slower but have greater success at finding the global minimum
regardless of the initial values of the variables
