Application of Genetic Algorithms to the Construction of Topologies for Multilayer Perceptrons

Abstract

In this paper we present a new approach for automatic topology optimization of backpropagation networks. It is based on a genetic algorithm. In contrast to other approaches it allows that two networks with different number of units can be crossed to a new valid “child” network. We applied this algorithm to a medical classification task, which is extremely difficult to solve. The results confirm, that optimization make sence, because the generated network outperform all fixed topologies.