## 2 Background and the Related Work
### Related Works
#### 2.2.3 GA-Based Encryption  
[PERSON] and [PERSON] (2018) introduced a GA-based symmetric key encryption technique utilizing substitution, genetic crossover, mutation, and key operations, for example,right shift and matrix addition for robust data transmission security, with a focus on simplicity, reliability, and effective encryption and decryption, using two-point crossover techniques.  
[PERSON] and [PERSON] [31] introduced a Java implementation of asymmetric key encryption with a genetic algorithm, focused on the importance of the crossover and mutation points for enhancing algorithmic effectiveness in the creation of the asymmetric key pair through a random process and application of a permutation technique for robustness, due to which 36-bit key was able to produce very strong encryption and decryption, and assurance against breaching was used.  
[PERSON] et al. [32] presented an advanced encryption technique using a customized GA with enhanced operators and local intelligence and achieved at least 80% greater efficiency in key generation while maintaining randomness compared to conventional GA methods. The study presented the algorithm customization, key parameter manipulation, binary data conversion, and result comparison. This contributes valuable insights into optimizing cryptographic security protocols.  
[PERSON] et al. [33] proposed a new framework that works with GA encryption to generate multiple encrypted cancelable biometric images. Compared with conventional algorithms, it shows the optical scanning holography algorithm on different biometric databases. It is expected to have better biometric security and better privacy preservation with lower risks of unauthorized access.  
[PERSON] et al. [34] introduced an advanced audio encryption and decryption technique using an enhanced GA. This integrates fission and fusion into traditional operators for robust keyspace and key parameter sensitivity. This effectively secures audio data while maintaining size and showcasing the method's success and limitations, suggesting future steganography applications.  
[PERSON] and [PERSON] [35] suggested a better method for concealing encrypted messages that makes use of protein motifs. By using genetic algorithms, logic gates, and protein motifs--which had several metrics that outperformed DNA-based algorithms, such as blindness, functional conservation, and capacity--the authors demonstrated encryption and steganography for enhanced data security and confidentiality. These works show how GA-based encryption can be used in both symmetric and asymmetric encryptions. The studies, however, do not explore GA's integration into SDN, where encryption must consider several criteria in addition to decryption accuracy, and instead focus on specific use cases with a single criterion of maintaining decryption accuracy.  
With SDN placing a strong focus on network security and encryption regulations, ideally managed by the SDN controller, the literature review shows that encryption techniques varied with respect to applications and compliance with various requirements. GA was highlighted for the flexibility it brought to the encryption of various data types together with parameter optimization, contributing to security. The implementation of GA-based hybrid encryption as an SDN policy remains under-explored regarding its impact on the encryption strength and effect of network execution.