Advancements in cloud computing and distributed computing have fostered research activities in Computer science. As a result, researchers have made significant progress in Neural Networks, Evolutionary Computing Algorithms like Genetic, and Differential evolution algorithms. These algorithms are used to develop clustering, recommendation, and question-and-answering systems using various text representation and similarity measurement techniques. In this research paper, Universal Sentence Encoder (USE) is used to capture the semantic similarity of text; And the transfer learning technique is used to apply Genetic Algorithm (GA) and Differential Evolution (DE) algorithms to search and retrieve relevant top N documents based on user query. The proposed approach is applied to the Stanford Question and Answer (SQuAD) Dataset to identify a user query. Finally, through experiments, we prove that text documents can be efficiently represented as sentence embedding vectors using USE to capture the semantic similarity, and by comparing the results of the Manhattan Distance, GA, and DE algorithms we prove that the evolutionary algorithms are good at finding the top N results than the traditional ranking approach.

翻译：云计算与分布式计算的进步推动了计算机科学领域的研究活动。研究人员因此在神经网络、遗传算法和差分进化算法等进化计算算法方面取得了显著进展。这些算法结合多种文本表示与相似性度量技术，被用于开发聚类、推荐及问答系统。本研究采用通用句子编码器（USE）捕获文本的语义相似性，并运用迁移学习技术将遗传算法（GA）与差分进化（DE）算法应用于基于用户查询搜索并检索相关的前N篇文档。所提出的方法在斯坦福问答数据集（SQuAD）上进行用户查询识别实验。最终，通过实验验证了USE能够将文本文档高效表示为句子嵌入向量以捕获语义相似性，并通过比较曼哈顿距离、GA与DE算法的结果，证明了进化算法在获取前N个结果方面优于传统排序方法。