A Heuristic Mutation Operator for Evolutionary Neural Network
Weight adaptation, node deletion and node addition are three key types of mutation operations for the evolutionary neural network(ENN). The determination of mutation rate and the selection of mutation type are two important issues for evolution, and they have a crucial impact on the performance of ENN. In order to improve the convergence speed and classification accuracy of ENN, a heuristic mutation operator (HMO), which evolves connection weights and network structure simultaneously, was proposed. An adaptive mutation rate is applied in the mutation operator, and the mutation type is selected heuristically from weight adaptation, node deletion and node addition. When the population is not evolved continuously for many generations, in order to jump from the local optima and extend the search space, the mutation rate will be increased and the mutation type will be changed. The experimental results with three classification problems show that the HMO achieves better performance than the traditional mutation operator (TMO) in terms of convergence speed and classification accuracy.
feedforward neural network evolutionary algorithm mutation operator mutation rate
Biying Zhang
College of Computer and Information Engineering Harbin University of Commerce Harbin, China
国际会议
秦皇岛
英文
249-252
2010-11-05（万方平台首次上网日期，不代表论文的发表时间）