综述遗传与差分进化算法在语义文档搜索中的应用
Survey of Genetic and Differential Evolutionary Algorithm Approaches to Search Documents Based On Semantic Similarity
- 聚焦遗传与差分进化算法解决文档语义相似性搜索
- 涵盖近年在大规模数据下的高效搜索进展
- 适合对智能检索与进化计算感兴趣的读者
在海量数据中识别语义相似文档是一项重大挑战。随着计算能力提升和大数据兴起，深度神经网络以及遗传算法、差分进化算法等进化计算方法取得了显著进展。本文综述了基于语义相似性的文档搜索领域最新成果，重点分析遗传算法与差分进化算法的应用现状与发展趋势。
原文摘要 · Abstract (English)
Identifying similar documents within extensive volumes of data poses a significant challenge. To tackle this issue, researchers have developed a variety of effective distributed computing techniques. With the advancement of computing power and the rise of big data, deep neural networks and evolutionary computing algorithms such as genetic algorithms and differential evolution algorithms have achieved greater success. This survey will explore the most recent advancements in the search for documents based on their semantic text similarity, focusing on genetic and differential evolutionary computing algorithms.
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