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Open AccessProceedings Article

Query improvement in information retrieval using genetic algorithms. A report on the experiments of the TREC project

Jing-Jye Yang, +2 more
- Iss: 500207, pp 31-58
TLDR
In this paper, an adaptive method using genetic algorithms to modify user queries, based on relevance judgments, was adapted for the Text Retrieval Conference (TREC) and shown to be applicable to large text collections, where more relevant documents are presented to users in the genetic modification.
Abstract
We have been developing an adaptive method using genetic algorithms to modify user queries, based on relevance judgments. This algorithm was adapted for the Text Retrieval Conference (TREC). The method is shown to be applicable to large text collections, where more relevant documents are presented to users in the genetic modification. The algorithm also shows some interesting phenomena, such as parallel searching. Further studies are planned to adjust the system parameters to improve its effectiveness

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Machine learning for information retrieval: neural networks, symbolic learning, and genetic algorithms

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The effects of fitness functions on genetic programming-based ranking discovery for Web search

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Crossover improvement for the genetic algorithm in information retrieval

TL;DR: The analysis of phenomena seen during the implementation of a GA for IR has brought a new crossover operation, which is introduced and compared with other learning methods.