The choice of crossover and mutation strategies plays a crucial role in the search ability, convergence efficiency and precision of genetic algorithms. In this paper, a new improved genetic algorithm is proposed by improving the crossover and mutation operation of the simple genetic algorithm, and it is verified by four test functions. Simulation results show that, comparing 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. Finally, the algorithm is applied to neural networks 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.
翻译:选择交叉和突变策略在遗传算法的搜索能力、趋同效率和精确度方面发挥着关键作用。在本文中,通过改进简单遗传算法的交叉和突变操作,提出了新的改良遗传算法,并通过四个测试功能加以验证。模拟结果表明,与其他三个主流群群情报优化算法相比,该算法不仅可以提高全球搜索能力、趋同效率和精确度,还可以提高在同一实验条件下达到最佳值的成功率。最后,该算法适用于神经网络对抗性攻击。应用结果显示,该方法不需要神经网络模型的结构和参数信息,而且可以在短时间内通过神经网络的分类和信任信息输出,以高度自信获得对抗样本。