On modeling of information retrieval concepts in vector spaces
ACM Transactions on Database Systems (TODS)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Query modification using genetic algorithms in vector space models
International Journal of Expert Systems
Journal of the American Society for Information Science
Applying genetic algorithms to query optimization in document retrieval
Information Processing and Management: an International Journal
An information-theoretic approach to automatic query expansion
ACM Transactions on Information Systems (TOIS)
European Research Letter: cross-language system evaluation: the CLEF campaigns
Journal of the American Society for Information Science and Technology
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A test of genetic algorithms in relevance feedback
Information Processing and Management: an International Journal
Multiple query evaluation based on an enhanced genetic algorithm
Information Processing and Management: an International Journal - Modelling vagueness and subjectivity in information access
Dependence Among Terms in Vector Space Model
IDEAS '04 Proceedings of the International Database Engineering and Applications Symposium
Set-based vector model: An efficient approach for correlation-based ranking
ACM Transactions on Information Systems (TOIS)
Optimisation methods for ranking functions with multiple parameters
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Query reformulation using automatically generated query concepts from a document space
Information Processing and Management: an International Journal
Improving the learning of Boolean queries by means of a multiobjective IQBE evolutionary algorithm
Information Processing and Management: an International Journal
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Nowadays, searching information in the web or in any kind of document collection has become one of the most frequent activities. However, user queries can be formulated in a way that hinders the recovery of the requested information. The objective of automatic query transformation is to improve the quality of the recovered information. This paper describes a new genetic algorithm used to change the set of terms that compose a user query without user supervision, by complementing an expansion process based on the use of a morphological thesaurus. We apply a stemming process to obtain the stem of a word, for which the thesaurus provides its different forms. The set of candidate query terms is constructed by expanding each term in the original query with the terms morphologically related. The genetic algorithm is in charge of selecting the terms of the final query from the candidate term set. The selection process is based on the retrieval results obtained when searching with different combination of candidate terms. The algorithm shows improvement over some other using standard collections.