# Artificial Intelligence in Cybersecurity: A Review and a Case Study
## 6 Enhancing Security in Low-Rate Wireless Networks
### Genetic Algorithm (GA)  
[PERSON], along with his students and colleagues, pioneered the GA during the 1960s and 1970s at the University of Michigan [31]. Inspired by natural selection and biological reproduction, GAs are evolutionary algorithms that have become popular optimization tools for various real-world applications. Mimicking natural selection (survival of the fittest) and biological reproduction processes, GAs develop optimal solutions (fittest individuals) progressively without relying on strict mathematical formulations. The optimal solution inherits the best characteristics (genes) from the fittest individuals in previous generations. Therefore, GAs are considered stochastic, nonlinear, and discrete event processes rather than mathematically guided algorithms. The simplest GA operates on a population of individuals represented by fixed-length bit strings. Selection criteria are used to choose a parent pool from the population for generating the next generation. The crossover and mutation operators introduce new candidate solutions into the population. The crossover operator produces new offspring by exchanging partial bit strings and inverting bits between two parents. The mutation operator randomly flips some genes of the new offspring. Each individual's fitness is evaluated using a fitness function. Finally, the fittest individual in the last generation is considered the optimal solution. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Symbol** & **Sequence (32,4)** \\ \hline
1 & 0000 & 110110011000110100100100101110 \\
2 & 1000 & 111011011001110000110100100010 \\
3 & 0100 & 001011101101110000111010101010010 \\
4 & 1100 & 0010001011101101100110000110101 \\
5 & 0010 & 01010010001011101101100111000011 \\
6 & 1010 & 0011010100100011110110110011100 \\
7 & 0110 & 11000011010000101111011011001 \\
8 & 1110 & 100111000011010010001011101101 \\
9 & 0001 & 10001100101010000001110111101 \\
10 & 1001 & 101110001100010110000001110111 \\
11 & 0101 & 011110001110001001011000000111 \\
12 & 1101 & 0111011110001100100100101100000 \\
13 & 0011 & 000001110111110001100100100110 \\
14 & 1011 & 0110000001111111011100011001001 \\
15 & 0111 & 10010110000001111101110001100 \\
16 & 1111 & 110010010100000111011110111000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PN Sequences of 4-bit Symbols. The GA begins by initializing a population with random candidate solutions. It then iteratively develops the optimal solution across generations. During the search process, the GA employs a set of genetic operators: selection, crossover, and mutation. The selection operator prepares the parents' pool for mating. Thus, the selection operator guides the GA to the optimal solution by preferring the fittest individuals over low-fitted ones as given in Figure 1. A crossover operator is a powerful tool for producing new offspring and improving the quality of individuals by swapping genes between the parents. Crossover operators can influence population diversity in complex ways. While they do not directly create entirely identical offspring (children) with their parents, they can favor specific combinations of existing traits. The mutation operator maintains the population diversity. The main idea is to change the allele of the child randomly. The mutation operator is controlled by a mutation probability that is kept as low as possible to avoid the GA behaving like a random search (Figure 2). The processes of the GA and its operators are given in Figure 3.  
GA borrows terminology from biology as it simulates biological processes. However, GA entities are much simpler than their biological counterparts. The fundamental GA terminologies are given as follows:  
Population: A set of candidate solutions. The population, a set of candidate solutions, allows the GA to explore various search space regions, facilitating global exploration. Therefore, the quality of the initial population significantly impacts GA performance. Chromosome/Individual: A candidate solution consisting of genes and their alleles.