Evolutionary design of MLP neural network architectures

In neural networks design, some parameters must be adequately set for an efficient performance to be achieved. The setting of these parameters is not a trivial task since different applications may require different values. The “trial-and-error” or traditional engineering approaches for this task do not guarantee that an optimal set of parameters is found. Evolutionary approaches have been recently proposed to overcome these problems. Genetic algorithms have been used as a heuristic search technique to find adequate neural architectures. This technique is based on natural selection and genetic mechanisms. It tries to produce better individuals from an initial set of individuals, based on a given criteria. This paper presents some results achieved by using this technique to search optimal neural architectures to solve real world credit analysis problems. In this paper the searches have been restricted to multilayer perceptron (MLP) networks which are feedforward fully-connected strictly-layered architectures