基于非均匀变异算子的状态空间进化算法  

State Space Evolutionary Algorithm Based on Non-uniform Mutation Operator

作　　者：凌哲 李茂军[1] LING Zhe;LI Mao-jun(School of Electrical and Information Engineering,Changsha University ofScience ＆Technology,Changsha 410114,China)

机构地区：[1]长沙理工大学电气与信息工程学院

出　　处：《计算机技术与发展》2018年第9期68-71,77,共5页Computer Technology and Development

基　　金：国家自然科学基金(61074018)

摘　　要：基于非均匀变异算子的状态空间进化算法(NUMSEA)是一种具有新颖性的实数编码进化算法。针对传统的状态空间进化算法转移矩阵的不足,设计一种基于非均匀变异等算子改进的状态空间转移矩阵。该矩阵突破了传统的状态空间转移矩阵,并在此基础上增加了非均匀变异算子以及非均匀算术交叉算子。通过提取分析每一代的最适值,再左乘新的转移矩阵,能够在原有的最优个体附件进行微小的搜索。进一步实现了转移矩阵随群体中个体适应度值的自适应变化,上一代群体中适值越大的个体在生成新个体时所作的贡献越大,算法的收敛速度也将增加。实验结果表明,改进算法不仅能提升对主效基因挖掘的精确性与平稳性,还能缩短对特征数据的提取时间。State space evolutionary algorithm based on non-uniform mutation （NUMSEA） is a novel evolutionary algorithm with realnumber coding. Aiming at the disadvantage of the transfer matrix of traditional state space model,we design an improved state spacetransfer matrix based on non-uniform mutation,which breaks through the traditional state space transfer matrix and on the basis adds non-uniform mutation operator and non-uniform arithmetic crossover operator. By extracting and analyzing the optimum value of each gen-eration,left multiplying by newly transfer matrix,we can conduct a small search in the original optimal individual attachment. The adap-tive change of transfer matrix is further implemented with the individual fitness value in groups. In the previous generation,the more a-daptable individuals in the group,the more contributions they make in generating new individuals,and the convergence speed of the algo-rithm will also increase. The experiment shows that the improved algorithm can not only improve the accuracy and stability of the majorgene mining,and shorten the time of extracting characteristic data.

关 键 词：状态空间算法 转移矩阵 适应度值 大数据 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]