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The choice of crossover and mutation strategies plays a crucial role in thesearchability, convergence efficiency and precision of genetic algorithms. Inthis paper, a novel improved genetic algorithm is proposed by improving thecrossover and mutation operation of the simple genetic algorithm, and it isverified by 15 test functions. The qualitative results show that, compared withthree other mainstream swarm intelligence optimization algorithms, thealgorithm can not only improve the global search ability, convergenceefficiency and precision, but also increase the success rate of convergence tothe optimal value under the same experimental conditions. The quantitativeresults show that the algorithm performs superiorly in 13 of the 15 testedfunctions. The Wilcoxon rank-sum test was used for statistical evaluation,showing the significant advantage of the algorithm at $95\%$ confidenceintervals. Finally, the algorithm is applied to neural network adversarialattacks. The applied results show that the method does not need the structureand parameter information inside the neural network model, and it can obtainthe adversarial samples with high confidence in a brief time just by theclassification and confidence information output from the neural network.

An Improved Genetic Algorithm and Its Application in Neural Network Adversarial Attack