用进化算法提升语义文档搜索效率，比传统方法更准。
Evolutionary Algorithms Approach For Search Based On Semantic Document Similarity
- 用USE提取语义向量，结合遗传与差分进化算法搜索相关文档。
- 在SQuAD数据集上，进化算法找前N条相关文档效果优于曼哈顿距离法。
- 适合需要精准语义检索的场景，如问答系统、智能搜索。
云计算与分布式计算的发展推动了计算机科学的研究。近年来，神经网络及遗传算法（GA）、差分进化算法（DE）等进化计算方法在文本聚类、推荐和问答系统中取得显著进展。本文采用通用句子编码器（USE）捕捉文本语义相似性，结合迁移学习技术，将GA与DE应用于基于用户查询的文档搜索与召回，目标是获取前N个最相关文档。实验在斯坦福问答数据集（SQuAD）上进行，结果表明：利用USE生成的句子嵌入向量能有效表征文本语义；通过对比曼哈顿距离、遗传算法和差分进化算法的表现，证明进化算法在寻找最优前N条结果方面优于传统排序方法。
原文摘要 · Abstract (English)
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.
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