  The main goal of this thesis is to provide an exploration of the use of
computational intelligence techniques to study the numerical solution of the
Ornstein-Zernike equation for simple liquids. In particular, a continuous model
of the hard sphere fluid is studied. There are two main proposals in this
contribution. First, the use of neural networks as a way to parametrize closure
relation when solving the Ornstein-Zernike equation. It is explicitly shown
that in the case of the hard sphere fluid, the neural network approach seems to
reduce to the so-called Hypernetted Chain closure. For the second proposal, we
explore the fact that if more physical information is incorporated into the
theoretical formalism, a better estimate can be obtained with the use of
evolutionary optimization techniques. When choosing the modified Verlet closure
relation, and leaving a couple of free parameters to be adjusted, the results
are as good as those obtained from molecular simulations. The thesis is then
closed with a brief summary of the main findings and outlooks on different ways
to improve the proposals presented here.
