Multi-level genetic programming for fault data clustering
Feng Qi, Haowei Lian, Jindong Zhu · 2017
Artificial intelligence theory is extensively employed in fault diagnosis, as the frequently used technologies, expert system and neural network, have their inherent disadvantages that have poor expansibility and unknown blackbox structure. Genetic Programming (GP), an improved evolution algorithm based on Genetic Algorithm (GA), could offset these insufficient for its explicit structure. Combining the main idea of hierarchical clustering, a new method based on GP is proposed. In this method, the multi-cluster problem is divided to many two-cluster problems, and GP serves as a classifier in two-cluster problem. Generally, the multi-level genetic programming classifier is expected to simplify the structure and improve the expansibility of classifier, and its effectiveness is proved in simulation experiment.