Multiple query evaluation based on an enhanced genetic algorithm
Inhaltsverzeichnis
Referenz
M. Boughanem, C. Chrisment, L. Tamine: Multiple query evaluation based on an enhanced genetic algorithm. Information Processing and Management, 39 (2003), pp. 215–231
DOI
http://dx.doi.org/10.1016/S0306-4573(02)00048-1
Abstract
Recent studies suggest that significant improvement in information retrieval performance can be achieved by combining multiple representations of an information need. The paper presents a genetic approach that combines the results from multiple query evaluations. The genetic algorithm aims to optimise the overall relevance estimate by exploring different directions of the document space. We investigate ways to improve the effectiveness of the genetic exploration by combining appropriate techniques and heuristics known in genetic theory or in the IR field. Indeed, the approach uses a niching technique to solve the relevance multimodality problem, a relevance feedback technique to perform genetic transformations on query formulations and evolution heuristics in order to improve the convergence conditions of the genetic process. The effectiveness of the global approach is demonstrated by comparing the retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation performed on a subset of TREC-4 using the Mercure IRS. Moreover, experimental results show the positive effect of the various techniques integrated to our genetic algorithm model.
Extended Abstract
Bibtex
@article{Tamine:2003:MQE:762177.762181,
author = {Tamine, Lynda and Chrisment, Claude and Boughanem, Mohand},
title = {Multiple Query Evaluation Based on an Enhanced Genetic Algorithm},
journal = {Inf. Process. Manage.},
issue_date = {March 2003},
volume = {39},
number = {2},
month = mar,
year = {2003},
issn = {0306-4573},
pages = {215--231},
numpages = {17},
url = {http://dx.doi.org/10.1016/S0306-4573(02)00048-1 http://de.evo-art.org/index.php?title=Multiple_query_evaluation_based_on_an_enhanced_genetic_algorithm},
doi = {10.1016/S0306-4573(02)00048-1},
acmid = {762181},
publisher = {Pergamon Press, Inc.},
address = {Tarrytown, NY, USA},
keywords = {genetic algorithm, information retrieval, relevance feedback},
}
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