Enhancing Cryptographic Resilience through Symmetric Encryption Algorithm Utilizing Variable Length Chromosomes Genetic Algorithm

Symmetric cryptography, particularly the Advanced Encryption Standard (AES), is widely used for secure data transmission due to its computational efficiency and strong encryption structure. However, limited internal randomness in AES makes it susceptible to advanced cryptanalytic attacks. To address this limitation, this research proposes an enhanced encryption approach that integrates Genetic Algorithm (GA) techniques into the AES framework to enhance randomness. The GA employs variable-length chromosomes and entropy-based fitness evaluation to evolve dynamic binary outputs through selection, crossover, and mutation operations. These evolved outputs are used to introduce controlled randomness into the encryption process, resulting in unpredictable and robust ciphertext. The expected outcome includes improved increased randomness, and stronger resistance to differential and linear attacks. In conclusion, the proposed GA-AES enhanced model offers a mechanism to strengthen symmetric encryption by introducing evolutionary randomness, making it more secure for modern data protection needs.