SEFNN:一种基于结构进化的前馈神经网络设计算法

SEFNN—A Feed-Forward Neural Network Design Algorithm Based on Structure Evolution

遗传算法是一种模拟自然选择和进化的随机搜索算法,它的搜索能够遍及整个解空间,容易得到全局最优解.目前主要的编码方式都是将结构和连接权值等信息编码成串式的基因,这不利于在遗传过程中保留个体的子结构信息,也难于设计兼顾基因型与表现型的遗传算子;在前馈神经网络的进化中引入BP训练方面,也不分良莠对所有后代进行训练,形成资源浪费.为克服这些问题,提出了一种基于结构进化的前馈神经网络设计算法SEFNN,该算法使用一种紧缩矩阵编码、新型结构化交叉算子、修订的变异算子和精英训练法则,充分考虑了基因型与表现型之间的关系,适当加大变异搜索速度,并采用选拔训练方式,从而提高了进化神经网络的效率.实验表明该算法获得的解无论在网络规模还是测试精度上都有优越的性能表现,并已应用于肺癌早期细胞病理诊断系统,具有良好的效果.

Genetic algorithm is a random search algorithm that simulates natural selection and evolution. It searches through the total solution space and can find the optimal solution globally over a domain. Recently, the popular encoding scheme is to encode the structure and weights, etc. into a string, which is not easy for the reservation of sub-structure during the process of genetic evolution. Generally, BP training scheme used in feed-forward neural network is to train all the offspring equally, which obviously wastes resources. A new method named SEFNN is proposed, which uses compact matrix encoding scheme, a new crossover operator, a properly modified mutate operator and rules of training elites. The efficiency of evolutionary feed-forward neural network is improved by properly considering the relationship between genotype and phenotype, thus improving the mutation speed and adopting a scheme of selective training. Experiments show that the proposed method can get good performance in accuracy. It has also found good application in a lung cancer diagnosis system.