Biological metaphors and the design of modular artificial neural networks

In this thesis, a method is proposed with which good modular artificial neural network structures can be found automatically using a computer program. A number of biological metaphors are incorporated in the method. It will be argued that modular artificial neural networks have a better performance than their non-modular counterparts. The human brain can also be seen as a modular neural network, and the proposed search method is based on the natural process that resulted in the brain: Genetic algorithms are used to imitate evolution, and L-systems are used to model the kind of recipes nature uses in biological growth. A small number of experiments have been done to investigate the possibilities of the method. Preliminary results show that the method does find modular networks, and that those networks outperform ‘standard’ solutions. The method looks very promising, although the experiments done were too limited to draw any general conclusions. One drawback is the large amount of computing time needed to evaluate the quality of a population member, and therefore in chapter 9 a number of possible improvements are given on how to increase the speed of the method, as well as a number of suggestions on how to continue from here.