On using genetic algorithms for multimodal relevance optimization in information retrieval
Inhaltsverzeichnis
Referenz
M. Boughanem, C. Chrisment, L. Tamine: On using genetic algorithms for multimodal relevance optimization in information retrieval. Journal of the American Society for Information Science and Technology, 53 (11) (2002), pp. 934–942
DOI
http://dx.doi.org/10.1002/asi.10119
Abstract
This article presents a genetic relevance optimization process performed in an information retrieval system. The process uses genetic techniques for solving multimodal problems (niching) and query reformulation techniques commonly used in information retrieval. The niching technique allows the process to reach different relevance regions of the document space. Query reformulation techniques represent domain knowledge integrated in the genetic operators structure to improve the convergence conditions of the algorithm. Experimental analysis performed using a TREC subcollection validates our approach.
Extended Abstract
Bibtex
@article {ASI:ASI10119,
author = {Boughanem, M. and Chrisment, C. and Tamine, L.},
title = {On using genetic algorithms for multimodal relevance optimization in information retrieval},
journal = {Journal of the American Society for Information Science and Technology},
volume = {53},
number = {11},
publisher = {Wiley Subscription Services, Inc., A Wiley Company},
issn = {1532-2890},
url = {http://dx.doi.org/10.1002/asi.10119 http://de.evo-art.org/index.php?title=On_using_genetic_algorithms_for_multimodal_relevance_optimization_in_information_retrieval},
doi = {10.1002/asi.10119},
pages = {934--942},
year = {2002},
}
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