摘要
遗传匹配追踪(Genetic matching pursuit,GMP)算法可提高寻找最佳原子速度,但选择算子降低了群体多样性,使算法易产生过早熟现象。为了在不影响收敛性的基础上提高多样性并使算法速度进一步提高,提出了将比例选择、最优保存策略、排序选择相结合的选择算子。所提算法分解人工合成信号和语音实际信号的时间相对于已有文献的GMP所用时间分别缩短了39.67%,7.36%,7.58%。仿真结果表明:这种改进选择算子的遗传匹配算法在残差能量和搜索时间两方面的改进是有效的。
Genetic matching pursuit（GMP） algorithm can improve the speed of finding the best atom,but the algorithm incline to be premature convergence because of the selection operator reducing the population diversity.Therefore,the method combining the proportional selection,the elitist strategy,and the rank selection is proposed to improve the population diversity without breaking down the convergence of the algorithm for speeding up the time of finding the best atom.The time of decomposing the synthetic signal and the actual voice signal is decreased by 39.67%,7.36%,and 7.58% compared with GMP in other essay.Simulation results show that the improved genetic matching algorithms is effective by the residual energy and the searching time.
出处
《数据采集与处理》
CSCD
北大核心
2011年第2期177-180,共4页
Journal of Data Acquisition and Processing
关键词
遗传算法
匹配追踪
选择算子
genetic algorithm
matching pursuit
selection operator
