Cooperative Coevolution for Learning Fuzzy Rule-Based Systems

In the last few years, the coevolutionary paradigm has shown an increasing interest thanks to its high ability to manage huge search spaces. Particularly, the cooperative interaction scheme is recommendable when the problem solution may be decomposable in subcomponents and there are strong interdependencies among them. The paper introduces a novel application of these algorithms to the learning of fuzzy rule-based systems for system modeling. Traditionally, this process is performed by sequentially designing their different components. However, we propose to accomplish a simultaneous learning process with cooperative coevolution to properly consider the tight relation among the components, thus obtaining more accurate models.