计算周期序列k-错线性复杂度的混合遗传算法

发布时间：2021-01-22 05:32

　　周期序列的线性复杂度及其稳定性是序列密码评价的重要度量指标.k-错线性复杂度是线性复杂度稳定性的一个重要评价指标.然而,目前对于大部分周期序列（除周期为2ⁿ、pⁿ、2pⁿ外）,尚无有效的算法求解其k-错线性复杂度.因此,本文提出了一种混合的遗传算法来近似计算任意周期序列的k-错线性复杂度.采用轮盘赌、最优保留策略、两点交叉和单点随机变异,并引入自适应算子来调整交叉概率和变异概率,以保证遗传算法的收敛性.通过并行计算适应度函数来提高算法的效率,同时与模拟退火算法相结合,加速算法收敛并避免早熟.结果表明:当k<8且周期小于256时,k-错线性复杂度的实验值仅比精确值高8%. 

【文章来源】：上海交通大学学报. 2020,54(06)北大核心

【文章页数】：8 页

【部分图文】：

序列s1的k-错线性复杂度实验值和准确值对比

s2={111001001011110001101100000011010110101 11110110110111111011100110101001011100101011 10001100010001010000001110001101000000101001 1},N=128s3={01011011011010000010011101010110110010 11010000011101011110010000010110110111011011 10101110011100110101010100100101111011011101 00101000101100000001100000111111010001111110 01100001110011010111010101111001100010010011 001010011111100001000011000101111000011010},N=256

s3={01011011011010000010011101010110110010 11010000011101011110010000010110110111011011 10101110011100110101010100100101111011011101 00101000101100000001100000111111010001111110 01100001110011010111010101111001100010010011 001010011111100001000011000101111000011010},N=256对比文献[16]的实验结果,周期为32的二元序列的5-错线性复杂度的实验结果比准确值平均高19.5%.而本文算法可以计算周期为256的二元序列的8-错线性复杂度,其实验值仅比准确值高8%.因此,本文算法不仅使可计算的N、k增加,还提高了算法的准确性和效率.

【参考文献】：
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本文编号：2992656