Abstract：In order to solve the shortcoming of relatively complicate and barely intelligible classification result of most conventional machine learningbased pattern recognition method, an evolutionary learning hypernetwork evolved by a genetic algorithm was proposed. Differed from the traditional hypernetwork based on a gradient descent or a hyperedge replacement scheme as system learning machine, a genetic algorithm was employed in the learning process of the proposed evolutionary learning hypernetwork. In the system learning process, the hyperedges of hypernetwork were divided into several subgroups for the individuals to be evolved independently with selection, crossover and mutation operations. The outstanding individuals were migrated to the neighbor subgroup in every generation. Every subgroup was evolved with genetic algorithm parallel, and eventually contributed to a hypernetworkbased classifier with the ability of decisionmaking. The evolved hypernetwork was used to classify the data set of acute leukemia, lung cancer and prostate. The experimental results show that the proposed approach leads to a very comparable classification performance with data set accuracies of 96.21% on acute leukemia, 99.26% on lung cancer and 96.09% on prostate, respectively. The learning results of the proposed hypernetwork are more readable than those of other traditional classification methods. The proposed scheme can efficiently discover significant high order interactions of gene pairs for cancer classification.
王进, 黄萍丽, 孙开伟, 蔡通. 基于演化学习超网络的微阵列数据分类[J]. 江苏大学学报（自然科学版）, 2014, 35(1): 56-62.
WANG Jin, HUANG Ping-Li, SUN Kai-Wei, CAI Tong. Microarray data classification based on evolutionary learning hypernetwork[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2014, 35(1): 56-62.
2014, Vol. 35 
