Technology Category
Search and Optimization: Evolutionary ComputationMachine Learning: Learning Preferences or RankingsData Mining & Knowledge Management: Intelligent Query Processing
Evolutionary Algorithms Approach For Search Based On Semantic Document Similarity
🤖 AI Summary
To address insufficient relevance in Top-N results for semantic document retrieval, this paper proposes a semantic matching framework integrated with evolutionary optimization. First, dense semantic vectors for queries and documents are generated using the Universal Sentence Encoder. Then, genetic algorithms (GA) and differential evolution (DE) are jointly and innovatively incorporated into the similarity ranking process to end-to-end optimize the matching function within the semantic space. This work is the first to systematically integrate both GA and DE into a semantic retrieval framework, thereby significantly enhancing ranking robustness and accuracy. Experimental evaluation on the SQuAD dataset demonstrates substantial improvements in Top-N accuracy over conventional static distance metrics—such as Manhattan distance—validating the effectiveness of evolutionary optimization in strengthening semantic matching performance.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Enhancing document retrieval using semantic similarity
Applying evolutionary algorithms for search efficiency
Comparing GA and DE with traditional ranking methods
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uses Universal Sentence Encoder
Applies Genetic Algorithm
Implements Differential Evolution
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