Fuzzy CoCo: Balancing accuracy and interpretability of fuzzy models by means of coevolution

In this chapter we present Fuzzy CoCo, a fuzzy modeling technique based on cooperative coevolution, conceived to provide high numeric precision (accuracy) while incurring as little a loss of linguistic descriptive power (interpretability) as possible. The search for interpretability is represented by several constraints taken into account when designing the evolutionary algorithm, which induce the drive for accuracy. Interpretability oriented fuzzy modeling must conduct two separate but intertwined search processes: (1) the search for membership functions, and (2) the search for rules. Towards this end, Fuzzy CoCo employs two coevolving species: database (membership functions) and rule base. Coevolution allows to overcome limitations presented by single-population evolutionary algorithms when confronted with fuzzy modeling, including stagnation, convergence to local optima, and computational costliness. We demonstrate the efficacy of Fuzzy CoCo by applying it to a hard, real-world problem—prediction of breast-cancer malignancy— obtaining excellent results.