Coevolving Different Knowledge Representations With Fine-grained parallel Learning Classifier Systems

This paper deals with the coevolution of different knowledge representations using fine-grained parallel learning classifier systems for data mining tasks. The objective is to demonstrate that a fine-grained parallel classifier systems can evolve individuals codifying different knowledge representations at the same time. This goal is achieved exploiting spatial relations of fine-grained parallel algorithms to favor the coevolution of knowledge representations, as well as extinction patterns. Experiments were performed with GALE2, a fine-grained parallel learning classifier system. Experiments focused on the diversity of the coevolved individuals, their classification accuracy, and the usefulness of the method proposed