Evolutionary Multi-Objective Based Hierarchical Interval Type-2 Beta Fuzzy System For Classification Problems

This study addresses evolutionary structure optimization and parameter tuning processes for evolving a proposed Hierarchical interval Type-2 Beta Fuzzy System (HT2BFS). The structure learning phase is performed in a multi-objective context by applying the Multi-Objective Extended Genetic Programming (MOEGP) algorithm. This phase aims to obtain a near-optimal structure of HT2BFS taking into account the optimization of two objectives, which are the accuracy maximization and the number of rules minimization. Moreover, a second parameter tuning phase is also performed in order to refine the parameters of the obtained near-optimal structure by applying the PSO-based Update Memory for Improved Harmony Search (PSOUM-IHS) algorithm. The system's performance is validated through two classification problems. Results prove the efficiency of the proposed approach.