The paper introduces a modified version of a genetic algorithm with aggressive mutation (GAAM) called fGAAM (fast GAAM) that significantly decreases the time needed to find feature subsets of a satisfactory classification accuracy. To demonstrate the time gains provided by fGAAM both algorithms were tested on eight datasets containing different number of features, classes, and examples. The fGAAM was also compared with four reference methods: the Holland GA with and without penalty term, Culling GA, and NSGA II. Results: (i) The fGAAM processing time was about 35% shorter than that of the original GAAM. (ii) The fGAAM was also 20 times quicker than two Holland GAs and 50 times quicker than NSGA II. (iii) For datasets of different number of features, classes, and examples, another number of individuals, stored for further processing, provided the highest acceleration. On average, the best results were obtained when individuals from the last 10 populations were stored (time acceleration: 36.39%) or when the number of individuals to be stored was calculated by the algorithm itself (time acceleration: 35.74%). (iv) The fGAAM was able to process all datasets used in the study, even those that, because of their high number of features, could not be processed by the two Holland GAs and NSGA II.

该论文介绍了一种称为 fGAAM（快速 GAAM）的具有侵略性变异 (GAAM) 的遗传算法的修改版本，该算法显着减少了找到具有令人满意的分类精度的特征子集所需的时间。为了证明 fGAAM 提供的时间增益，这两种算法都在包含不同数量的特征、类和示例的八个数据集上进行了测试。fGAAM 还与四种参考方法进行了比较：有和没有惩罚项的 Holland GA、Culling GA 和 NSGA II。结果：（i）fGAAM 的处理时间比原始 GAAM 的处理时间短约 35%。(ii) fGAAM 也比两个 Holland GA 快 20 倍，比 NSGA II 快 50 倍。(iii) 对于不同数量的特征、类别和示例的数据集，另外数量的个体，存储以供进一步处理，提供了最高的加速度。平均而言，当存储来自最后 10 个种群的个体时（时间加速度：36.39%）或当要存储的个体数量由算法本身计算时（时间加速度：35.74%）获得最佳结果。(iv) fGAAM 能够处理研究中使用的所有数据集，即使是那些由于其大量特征而无法由两个 Holland GA 和 NSGA II 处理的数据集。