A multiple population XCS: Evolving condition-action rules based on feature space partitions

XCS is an accuracy-based machine learning technique, which combines reinforcement learning and evolutionary algorithms to evolve a set of classifiers (or rules) for pattern classification tasks. In this paper, we investigate the effects of alternative feature space partitioning techniques in a multiple population island-based parallel XCS. Here, each of the isolated populations evolve rules based on a subset of the features. The behavior of the multiple population model is carefully analyzed and compared with the original XCS using the Boolean logic multiplexer problem as a test case. Simulation results show that our multiple population XCS produced better performance and better generalization than the single population XCS model, especially when the problem increased in size. A caveat, however, is that the effectiveness of the model was dependent upon the feature space partitioning strategy used.