Evolving neural network learning behaviours with set-based chromosomes

This paper describes a set-based chromosome for describing neural networks. The chromosome speciies sets of neurons with particular functions, and the interconnections between sets. Each set is updated in order, as are the neurons in that set, in accordance with a simple pre-speciied algorithm. This allows all details of a neural architecture, including its learning behaviour to be speciied in a simple and purely declarative manner. To evolve a learning behaviour for a particular network architecture, certain details of the architecture are pre-speciied by deening a chromosome template, with some of the genes xed, and others allowed to vary. In this paper, a learning perceptron is evolved, by xing the feedforward and error-computation parts of the chromosome, then evolving the feedback part responsible for computing weight updates. Using this methodology, learning behaviours with similar performance to the delta rule have been evolved.