School of Computer Science, Yangtze University, Jingzhou, China.
School of Electronic & Information, Yangtze University, Jingzhou, China.
PLoS One. 2022 May 5;17(5):e0267970. doi: 10.1371/journal.pone.0267970. eCollection 2022.
The choice of crossover and mutation strategies plays a crucial role in the searchability, convergence efficiency and precision of genetic algorithms. In this paper, a novel improved genetic algorithm is proposed by improving the crossover and mutation operation of the simple genetic algorithm, and it is verified by 15 test functions. The qualitative results show that, compared with three other mainstream swarm intelligence optimization algorithms, the algorithm can not only improve the global search ability, convergence efficiency and precision, but also increase the success rate of convergence to the optimal value under the same experimental conditions. The quantitative results show that the algorithm performs superiorly in 13 of the 15 tested functions. The Wilcoxon rank-sum test was used for statistical evaluation, showing the significant advantage of the algorithm at 95% confidence intervals. Finally, the algorithm is applied to neural network adversarial attacks. The applied results show that the method does not need the structure and parameter information inside the neural network model, and it can obtain the adversarial samples with high confidence in a brief time just by the classification and confidence information output from the neural network.
交叉和变异策略的选择在遗传算法的搜索能力、收敛效率和精度方面起着至关重要的作用。本文通过改进简单遗传算法的交叉和变异操作，提出了一种新的改进遗传算法，并通过 15 个测试函数进行了验证。定性结果表明，与其他三种主流的群体智能优化算法相比，该算法不仅可以提高全局搜索能力、收敛效率和精度，而且可以在相同的实验条件下增加收敛到最优值的成功率。定量结果表明，该算法在 15 个测试函数中的 13 个函数中表现优异。Wilcoxon 秩和检验用于统计评估，表明该算法在 95%置信区间内具有显著优势。最后，将该算法应用于神经网络对抗攻击。应用结果表明，该方法不需要神经网络模型的结构和参数信息，仅通过神经网络的分类和置信信息输出，就可以在短时间内获得置信度较高的对抗样本。
