Abstract This article describes an indirectly encoded evolutionary learning algorithm to train morphological neural networks. The indirect encoding method is an algorithm in which the training of the neural network is done by finding the solution without considering the exact connectivity of the network. Looking for the set of weights and architecture in a reduced search space, this simple, but powerful training algorithm is able to evolve to a feasible solution using up to three layers required to perform the pattern classification. This type of representation provides the necessary compactness required by large networks. The algorithm was tested using Iris Fisher data and a prototype was written using Matlab
