## 3 VFS Algorithm Analysis
### Content Relevance Score \(D_{3}\)
#### 3.3.2 Unrelated Vector Screening Module  
Genetic algorithms [24] are optimization methods derived from biological evolution theory, simulating natural selection and genetic mechanisms to search and optimize the solution space of problems. The core idea of this algorithm is to establish a population composed of a series of individuals, each representing a potential solution to the problem. Through multiple generations of iteration, experiencing genetic operations such as selection, crossover, and mutation, the population continuously improves, thereby gradually approaching the optimal solution to the problem.  
In this study, genetic algorithms are applied to optimize the vector representation of text analysis. Specifically, the operation begins by randomly generating 200 vectors of 200 dimensions each, forming the initial population. These vectors represent potential solutions used to filter out the vectors most suitable for representing irrelevant word vectors (\(x_{c}\)). We define an adaptive function \(F(v)\) to assess the fitness of each vector relative to the problem. Through multiple iterations, this function guides the evolution of the population, thereby filtering out the optimal vector representation for the subsequent evaluations of text content relevance.  
Figure 4: Skip-gram model.  
Figure 3: CBOW model.  
The application of this method has improved the accuracy and efficiency of our algorithm, ensuring the relevance and professionalism of model outputs within specific fields.  
\[F(v)=\left|\frac{1}{|V|}\sum_{v_{i}\in V}Similarity(v,v_{i})\right| \tag{6}\]  
\[Similarity(a,b)=\cos(\theta)=\frac{a\cdot b}{\|a\|\|b\|} \tag{7}\]  
\[|V|=n \tag{8}\]  
In this paper, each individual in the initially generated random population is first compared with all word vectors in the vectorized vocabulary \(V\), and the fitness score of each individual in the population is calculated using the adaptive function \(F(v)\). Subsequently, these scores are sorted, and the top 80 individuals with the highest adaptive scores are selected as the parents for the next generation of the population.  
By combining the 100-dimensional features from both parents, new individuals in the population are generated. This process produces a total of 120 new individuals, who, together with the original parent individuals, form a new population. To increase the diversity of vectors and enhance the likelihood of high-quality target vectors emerging, a 5-dimensional genetic mutation is introduced into the offspring population.  
In the implementation of the genetic algorithm, the convergence speed of the algorithm needs to be considered. The number of generations required varies for vectorized vocabularies of different scales and dimensions. In this study, based on practical requirements, 50 generations were chosen as the iteration number for the genetic algorithm, which has been proven to yield relatively good target vector individuals, meeting the needs of the research outcomes.  
Through this method, the performance of the algorithm can be effectively improved, ensuring that the generated vectors accurately reflect the relevance of the text content, thereby enhancing the effectiveness of large language models in vertical field applications.