Crossover Improvement for the Genetic Algorithm in Information Retrieval.
Fuente:
ERIC Institute of Education Sciences
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| Autore principale: | |
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| Natura: | Recurso educativo Open Access |
| Lingua: | en |
| Pubblicazione: |
1998
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| _version_ | 1867181575365459968 |
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| author | Vrajitoru, Dana |
| author_facet | Vrajitoru, Dana Vrajitoru, Dana |
| collection | Education Resources Information Center |
| contents | Crossover Improvement for the Genetic Algorithm in Information Retrieval. Vrajitoru, Dana Algorithms Comparative Analysis Information Retrieval Information Seeking Information Systems Learning Library Collections User Needs (Information) In information retrieval (IR), the aim of genetic algorithms (GA) is to help a system to find, in a huge documents collection, a good reply to a query expressed by the user. Analysis of phenomena seen during the implementation of a GA for IR has led to a new crossover operation, which is introduced and compared to other learning methods. (Author/AEF) |
| format | Recurso educativo Open Access |
| id | eric_EJ574018 |
| institution | ERIC Institute of Education Sciences |
| language | en |
| publishDate | 1998 |
| record_format | eric |
| spellingShingle | Crossover Improvement for the Genetic Algorithm in Information Retrieval. Vrajitoru, Dana Algorithms Comparative Analysis Information Retrieval Information Seeking Information Systems Learning Library Collections User Needs (Information) Crossover Improvement for the Genetic Algorithm in Information Retrieval. Vrajitoru, Dana Algorithms Comparative Analysis Information Retrieval Information Seeking Information Systems Learning Library Collections User Needs (Information) In information retrieval (IR), the aim of genetic algorithms (GA) is to help a system to find, in a huge documents collection, a good reply to a query expressed by the user. Analysis of phenomena seen during the implementation of a GA for IR has led to a new crossover operation, which is introduced and compared to other learning methods. (Author/AEF) |
| title | Crossover Improvement for the Genetic Algorithm in Information Retrieval. |
| topic | Algorithms Comparative Analysis Information Retrieval Information Seeking Information Systems Learning Library Collections User Needs (Information) |
| url | https://eric.ed.gov/?id=EJ574018 |