On the use of rule-sharing in learning classifier system ensembles

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.