Comparing genetic operators with gaussian mutations in simulated evolutionary processes using linear systems
Comparing genetic operators with gaussian mutations in simulated evolutionary processes using linear systems
复制标题
使用线性系统在模拟进化过程中比较遗传算子与高斯突变
DOI：
10.1007/bf00203032
复制
发表时间：
1990
影响因子：
1.9
通讯作者：
J. W. Atmar
中科院分区：
文献类型：
--
作者：
D. Fogel;J. W. Atmar
进化优化被认为是一种通过自动发现来产生机器学习的方法。已经提出了特定的遗传操作(例如，交叉和倒置)来突变编码表达行为的结构。这些运算的效率是通过一系列旨在解线性方程组的实验来评估的。结果表明，这些遗传算子并不能与更简单的随机变异相比。
Evolutionary optimization has been proposed as a method to generate machine learning through automated discovery. Specific genetic operations (e.g. crossover and inversion) have been proposed to mutate the structure that encodes expressed behavior. The efficiency of these operations is evaluated in a series of experiments aimed at solving linear systems of equations. The results indicate that these genetic operators do not compare favorably with more simple random mutation.