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

TL;DR

This survey reviews recent advancements in using genetic and differential evolutionary algorithms for document search based on semantic similarity, highlighting their effectiveness in handling large-scale data with deep neural networks.

Contribution

It provides a comprehensive overview of the latest evolutionary computing techniques applied to semantic document search, emphasizing recent progress and applications.

Findings

Genetic algorithms and differential evolution improve semantic search accuracy.

Evolutionary algorithms are increasingly integrated with deep neural networks.

Recent methods demonstrate scalability for large datasets.

Abstract

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.