Rule networks in learning classifier systems
Karthik Kuber, Stuart W. Card, Kishan G. Mehrotra, Chilukuri Krishna Mohan · 2014
Interrelationships between rules can be used to develop network models that can usefully represent the dynamics of Learning Classifier Systems. We examine two different kinds of rule networks and study their significance by testing them on the 20-mux problem. Through this experimentation, we establish that there is latent information in the evolving rule networks alongside the usual information that we gain from the XCS. We analyze these interrelationships using metrics from Network Science. We also show that these network measures behave as reliable indicators of rule set convergence.