Order Optimization of Evolutionary Hypernetworks Using Genetic Algorithm

Hyper networks consist of a large number of hyper edges that represent high-order features sampled from training sets. The order of hyper edges is an important parameter of a hyper network model and influences the performance of the hyper network classification system. Previous studies determine the parameter by the artificial exhaustive search method before evolutionary learning. Not only is the approach time-consuming, but also the traditional hyper network lacks generalization. In this study, a genetic algorithm is employed to optimize the order of hyper networks. The proposed method is tested on the acute leukemia and the colon cancer dataset. Experimental results show that the proposed approach can find the global optimal order automatically. Also, a comparative study on five classification algorithms shows that the improved hyper network model achieves a comparable classification performance.