mlsanityData to 14 Oct 2026

Survey of Genetic and Differential Evolutionary Algorithm Approaches to Search Documents Based On Semantic Similarity

arXiv 2507.11751 ↗First posted 15 Jul 2025cs.NE cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 27 references that are in the map. First posted on arXiv 15 Jul 2025.

Builds on

3 of 27 references.

References · 3

  1. 2024Is Cosine-Similarity of Embeddings Really About Similarity?cited by 89
  2. 2020Evolution of Semantic Similarity -- A Surveycited by 83
  3. 2025Evolutionary Algorithms Approach For Search Based On Semantic Document Similaritycited by 2

Built on by

Not cited yet.