Evolving Neural Networks: Using an evolutionary algorithm to create multi-class classification networks

Abstract

Designing the topology of a neural network can be a difficult task, as there is no reliable method of producing such a network. Studies have shown that the topology and connection weights of a neural network can be obtained through evolutionary algorithms. However, research has focused primarily on networks that generate real-valued output. In this thesis, we ask the question if evolutionary algorithms can be used to produce neural networks suited for multi-class classification problems. This is non-trivial, as these kinds of networks generally feature more nodes and therefore are more complex and harder to generate. We present an algorithm that generates and adapts such neural networks using evolutionary algorithms. Results from testing the algorithm on four different datasets show that the algorithm is capable of producing networks for relatively simple datasets. The algorithm was, however, unable to produce useful networks for the most complex dataset.