Coevolutionary genetic fuzzy systems: a hierarchical collaborative approach

Abstract

In this paper a coevolutionary genetic approach is devised to support hierarchical, collaborative relations between individuals representing different parameters of Takagi–Sugeno fuzzy models. The coevolutionary approach assumes species to mean partial solutions of fuzzy modeling problems organized into four hierarchical levels. Individuals at each hierarchical level encode membership functions, individual rules, rule-bases and fuzzy systems, respectively. A shared fitness evaluation scheme is used to measure the performance of each individual. Constraints are observed and particular targets are defined throughout the hierarchical levels, with the purpose of promoting the occurrence of valid individuals and inducing rule compactness, rule base consistency, and partition set visibility. The performance of the approach is evaluated via an example of function approximation with noisy data, and a nonlinearly separable classification problem.