USING MULTI-ANGLES EVOLUTIONARY ALGORITHMS FOR TRAINING TSK-TYPE NEURO-FUZZY NETWORKS
Pei-Chia Hung, Sheng‐Fuu Lin, Yung Hsu · 2012
Abstract. The development of a global-based method for building robust neuro-fuzzy networks has become an interesting issue. Among the various building methods, the evo-lutionary algorithms provide robust ways increasing the chances of meeting the optimal solution. However, evolutionary algorithms may only use a single angle to evaluate the searching space to obtain the optimal solutions. It implies that they may slowly or even hardly meet the optimal solution. Thus, the current study provides a novel architec-ture that uses multiple angles for evaluating the searching space. More specically, the novel architecture adopts multiple angles to improve the evolutionary process by dynam-ically adjusting the searching space. By doing so, the proposed architecture can increase the chances of meeting the optimal solution. As shown in the results, the proposed ar-chitecture outperforms other existing evolutionary algorithms. Based on the results, a framework is proposed to build a benchmark for developing evolutionary algorithms that consider the multiple angles of the solution space.