Crossover improvement for the genetic algorithm in information retrieval
Inhaltsverzeichnis
Referenz
D. Vrajitoru: Crossover improvement for the genetic algorithm in information retrieval. Information Processing and Management, 34 (4) (1998), pp. 405–415
DOI
http://dx.doi.org/10.1016/S0306-4573(98)00015-6
Abstract
Genetic algorithms (GAs) search for good solutions to a problem by operations inspired from the natural selection of living beings. Among their many uses, we can count information retrieval (IR). In this field, the aim of the GA is to help an IR system to find, in a huge documents text collection, a good reply to a query expressed by the user. The analysis of phenomena seen during the implementation of a GA for IR has brought us to a new crossover operation. This article introduces this new operation and compares it with other learning methods.
Extended Abstract
Bibtex
@article{Vrajitoru1998405,
title = "Crossover improvement for the genetic algorithm in information retrieval ",
journal = "Information Processing & Management ",
volume = "34",
number = "4",
pages = "405 - 415",
year = "1998",
note = "",
issn = "0306-4573",
doi = "http://dx.doi.org/10.1016/S0306-4573(98)00015-6",
url = "http://www.sciencedirect.com/science/article/pii/S0306457398000156 http://de.evo-art.org/index.php?title=Crossover_improvement_for_the_genetic_algorithm_in_information_retrieval",
author = "Dana Vrajitoru"
}
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Links
Full Text
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