计算机科学 ›› 2025, Vol. 52 ›› Issue (12): 351-357.doi: 10.11896/jsjkx.241200039
赵海霞1,3, 李鑫1, 韦永壮2
ZHAO Haixia1,3, LI Xin1, WEI Yongzhuang2
摘要: 对称密码算法通常采用安全指标良好的平衡布尔函数作为核心部件,以保障整个算法的安全性。使用启发式算法进行搜索是获得优良平衡布尔函数的一个重要途径。对此,设计了Rank排序混合遗传算法,用于搜索高非线性度、低自相关绝对值指标的平衡布尔函数。与传统的遗传算法相比,Rank排序混合遗传算法在交叉阶段设计了交叉保护策略,以保障子代的平衡性;在选择步骤,采用基于适应度函数值的精英选择策略,以防止优秀个体流失;在进入下一轮迭代之前,设计了Rank排序环节,以增大下一轮进行交叉的个体间的差异,提高生成优秀子代的可能性,降低算法陷入局部最优解的风险。实验结果表明,以6至14元的偶变元数的布尔函数为搜索对象,使用Rank排序混合遗传算法均可搜索得到非线性度严格几乎最优、自相关绝对值指标低的平衡布尔函数。
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