自适应遗传优化BP网络的研究与应用

针对遗传算法易出现种群多样性被破坏、早熟收敛的问题,在Srinivas的自适应遗传算法(AGA)的基础上,引入种群多样性的度量参数,提出一种改进的自适应遗传算法(MAGA),利用种群多样性和适应度的变化趋势调整交叉和变异概率,继而提出基于MAGA优化BP(back-propagation)神经网络的流量分类方法(MAGA+BP),兼顾了MAGA和BP算法分别在搜索全局和局部最优解方面的优势. 在剑桥大学共享的网络流量数据上进行了仿真实验,结果表明,MAGA较好地维持了种群的多样性,克服了AGA早熟收敛的问题,搜索到最优解的适应度提高了10.17%, MAGA+BP方法对流量数据具有较好的分类效果.

Research and Application of an Optimized BP Neural Network Based on Adaptive Genetic Algorithm

The population diversity of conventional genetic algorithm can be easily destroyed, which further leads to premature convergence. To solve this problem, based on adaptive genetic algorithm (AGA) proposed by Srinivas, a modified adaptive genetic algorithm (MAGA) is presented by introducing a parameter measuring the population diversity. In this way, the probabilities of crossover and mutation are adjusted automatically according to both population diversity and the trends of fitness values. Since MAGA and back-propagation (BP) algorithm are good at searching global and local optimum respectively, an optimized BP neural network based on MAGA (MAGA+BP) is then presented for traffic classification. The Internet traffic dataset provided by university of Cambridge is introduced for experimental validation. Results show that: MAGA shows better performance on maintaining population diversity, overcomes the premature convergence of AGA and improves the fitness value of resulting optimum by 10.17%; MAGA+BP shows a better performance on Internet traffic classification.