An Improved Genetic Algorithm and Its Application in Neural Network Adversarial Attack
Dingming Yang, Zeyu Yu, Hongqiang Yuan, Yanrong Cui

TL;DR
This paper introduces an improved genetic algorithm with enhanced crossover and mutation strategies, demonstrating superior performance in optimization tasks and effective application in generating neural network adversarial attacks without needing internal model details.
Contribution
The paper proposes a novel genetic algorithm with improved operators and applies it successfully to neural network adversarial attack generation, outperforming existing swarm algorithms.
Findings
The algorithm outperforms three mainstream swarm algorithms on 13 of 15 test functions.
Statistical tests confirm the algorithm's significant advantage at 95% confidence.
It efficiently generates high-confidence adversarial samples without internal neural network information.
Abstract
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 …
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Sensor and Control Systems · Simulation and Modeling Applications · Advanced Algorithms and Applications
MethodsTest
