An Analysis of Genetic Algorithms in Cryptography

Genetic algorithms (GAs) have emerged as powerful tools for optimizing cryptographic components such as key generation schemes, pseudorandom number generators (PRNGs), and substitution boxes (S-boxes). This paper presents a comprehensive analysis of how GAs contribute to enhancing cryptographic strength by improving statistical randomness, nonlinearity, and resistance to common attacks. Several representative approaches are examined, ranging from classical GA-based PRNGs to advanced hybrid models integrating neural networks and adversarial learning. A comparative evaluation highlights the trade-offs between randomness quality, algorithmic complexity, and implementation feasibility. Additionally, the paper proposes a framework for assessing GA-based cryptographic systems using well-established metrics such as entropy, differential uniformity, and algebraic degree. The findings confirm that GAs offer a flexible and effective foundation for designing secure, adaptive, and high-performance cryptographic mechanisms.