On the use of Rule-Sharing in Learning Classifier System Ensembles
Larry Bull, Matthew Studley, Tony Bagnall, Ian M. Whittley · 2005
This paper presents an investigation into exploiting the population-based nature of learning classifier systems for their use within highly-parallel systems. In particular, the use of simple accuracy-based learning classifier systems within the ensemble machine approach is examined. Results indicate that inclusion of a rule migration mechanism inspired by parallel genetic algorithms is an effective way to improve learning speed.