  This paper deals with the distributed processing in the search for an optimum
classification model using evolutionary product unit neural networks. For this
distributed search we used a cluster of computers. Our objective is to obtain a
more efficient design than those net architectures which do not use a
distributed process and which thus result in simpler designs. In order to get
the best classification models we use evolutionary algorithms to train and
design neural networks, which require a very time consuming computation. The
reasons behind the need for this distribution are various. It is complicated to
train this type of nets because of the difficulty entailed in determining their
architecture due to the complex error surface. On the other hand, the use of
evolutionary algorithms involves running a great number of tests with different
seeds and parameters, thus resulting in a high computational cost
