## 3 Proposed Methodology
### _Rfo-Svm_
#### 3.5.3 Genetic Algorithm-Based Data Allocation  
The data allocation process using a Genetic Algorithm involves evaluating the fitness of each solution based on minimizing communication costs, maximizing load balancing, or reducing data access latency. The fitness function captures the objectives and constraints of the data fragment allocation problem. The Genetic Algorithm (GA) is employed to evolve  
Figure 2: Proposed architecture of RFO-SVM framework. a population of solutions. Genetic operators, including selection, crossover, and mutation, are applied to generate new offspring solutions. Selection Operator: The selection operator determines which individuals in the population are selected as parents for producing offspring. For selecting individuals based on their fitness values, the following is used:  
\[\rho_{i}=\frac{F_{i}}{\sum F_{j}} \tag{28}\]  
where \(\rho_{i}\) is the probability of selecting individual i as a parent, \(F_{i}\) is the fitness value of individual i, and \(\sum F_{j}\) is the sum of fitness values of all individuals in the population. The fitness of each individual within each generation in the population is computed using the fitness function. Selection and crossover operations are performed to generate offspring solutions. Parents for crossover are selected based on their fitness values. Crossover is performed by randomly selecting a crossover point and swapping genetic data between two parents and is formulated as  
\[\phi=CO_{p1,p2} \tag{29}\]  
where \(\phi\) represents the newly generated offspring solution, and \(CO_{p1,p2}\) is a function that combines the genetic data from parent1 and parent2. The Roulette Wheel Selection method is utilized for selecting the parents for the next generation. This selection is probability-based, where each individual's chance of being selected is proportional to its fitness. This method simulates a roulette wheel where the size of each segment is aligned with the fitness of the individuals, promoting higher fitness individuals to be more likely chosen, thereby steering the population towards promising regions of the search space. Mutation Operator  
Mutation is performed on the offspring solutions to introduce new genetic data in exploring different regions of the search space. The population and offspring solutions are combined, and their fitness values are computed by  
\[\mu_{s}=\mu_{i} \tag{30}\]  
where \(\mu_{s}\) is the individual solution after mutation, and \(\mu_{i}\) is a function that applies random changes to the genetic data of an individual. Survival selection is performed by selecting the top individuals in the combined population. The best allocation of data points to clusters is generated for each generation. After all generations are completed, the allocation with the lowest fitness score is selected as the final allocation. Here, in Gaussian mutation, the changes to the gene values are made according to a Gaussian distribution. This method is used for real-valued genes and is beneficial in fine-tuning solutions, allowing for small, stochastic tweaks that help in escaping local optima. Crossover Operator  
The uniform crossover technique is applied wherein a binary mask determines the genes to be inherited from each parent. This method offers the flexibility of combining genes from parent chromosomes, contributing to diversity in the gene pool. It is particularly effective in exploring new areas of the solution space without being confined to the building blocks defined by the parents' structure. The number of children generated in each generation is a critical parameter that influences the genetic diversity and convergence speed of the GA. In our implementation, we maintain a balanced approach where the number of children is set equal to the number of parents, ensuring a steady state of population size across generations. Parameters and Configuration  
Population Size: Determined based on preliminary tests to balance computational efficiency and algorithm performance.