## 3 Proposed Methodology
### Feature Selection Techniques
#### 3.3.2 Genetic Algorithm (GA)  
GA is known as a search-based optimization technique inspired by Genetics and Natural Selection [42]. GA has been widely used not only for feature selection but also for optimizing the hyper-parameters of an Artificial Neural Network (ANN), such as weight [40; 43; 44; 45; 46; 47; 48; 49; 50; 51]. As shown in Figure 2, a GA algorithm starts with an initial population, which is a subset of all the possible solutions (Also known as individuals) to the given problem [42]. Each individual has a set of genes, represented by a string of zeros and ones [42]. To evaluate the quality of a solution, a fitness value is assigned to each individual. Further various strategies can be applied to select the best individuals known as parents [42]. After the parent selection step, the variation operations such as mutation and crossover [42] are applied to generate new off-springs [42]. Finally, these off-springs replace the existing individuals in the population, and the process is repeated until reaching the stopping criterion. As shown in Figure 3, to adapt GA technique with this study, the steps below are proposed:  
* Binary representation: In the feature selection step, each of the elements in the feature vector may or may not be selected. Therefore, a string is used to represent the selected and discarded features. In the given string, each 1 indicates that the corresponding feature to the index is chosen, while each 0 determines the discarded features [42]. * Population model: In a steady state of GA technique, one or more off-spring is generated in each iteration, and they replace one or more individuals from the population [42]. * Population initialization: This study considers a random initialization, in which \(k\) bits of the string is randomly set to 1. In other words, \(k\) features are chosen out of the n-dimensional space of features [42]. * Parent selection: The \"Rank Selection\" approach is used for selecting parents since the individuals in the population have very close fitness values. In this work, the fitness value is the classificationaccuracy which is computed by specifying the number of True Detection (\(N_{TD}\)) and the number of False Detection (\(N_{FD}\)) [21]: \[Accuracy=\frac{N_{TD}}{N_{TD}+N_{FD}}*100\] (12) Therefore, each individual in the population is ranked according to their fitness, and the parent selection depends on the rank of each individual, not on the fitness value [42]. * Mutation: The swap mutation is deployed, in which two random positions on the chromosome are selected, and their values interchanged. It should be noted that in this application, the crossover is the same as the mutation. In other words, if two parents are selected, to exchange the elements in the selected indices (single point or other crossover methods), while preserving the number of 1s in the solution, so it accurately mimics the mutation process [49]. * Survival selection: The survival policy determines which individuals should be maintained in the next generation. However, this is a crucial step as it must ensure that the fitter individuals are not kicked out of the population while maintaining diversity in the same population. In this work, a fitness-based selection is used, in which the children tend to replace the least fit individuals in the population [49]. * Termination criterion: The algorithm is terminated when a set number of generations is reached. Figure 4 shows the variation in the best fitness values during 100 generations for RFI classification using five features. The result presents the improvement in the classification accuracy of over 100 number of generations up to 99.81%. Moreover, the best fitness value corresponds to the chromosome [001000111], which indicates that the more informative features are the standard  
Figure 2: General framework of GA algorithm. deviation of the received signal and average power of the 4 th wavelet coefficients (\(D_{1}\) to \(D_{4}\)) using Daubechies 10 (dB10) wavelet. Figure 3: The proposed GA algorithm for feature selection.