Applying multi-objective evolutionary algorithms to the automatic learning of extended Boolean queries in fuzzy ordinal linguistic information retrieval systems
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- @Article{LopezHerrera20092192,
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author = "A. G. Lopez-Herrera and E. Herrera-Viedma and
F. Herrera",
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title = "Applying multi-objective evolutionary algorithms to
the automatic learning of extended {Boolean} queries in
fuzzy ordinal linguistic information retrieval
systems",
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journal = "Fuzzy Sets and Systems",
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volume = "160",
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number = "15",
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pages = "2192--2205",
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year = "2009",
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note = "Special Issue: The Application of Fuzzy Logic and Soft
Computing in Information Management",
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ISSN = "0165-0114",
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DOI = "doi:10.1016/j.fss.2009.02.013",
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URL = "http://www.sciencedirect.com/science/article/B6V05-4VPM59B-4/2/21a5a32bf1a659a371ce5c4d320da182",
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keywords = "genetic algorithms, genetic programming, MOGP,
Information retrieval systems, Inductive query by
example, Multi-objective evolutionary algorithms, Query
learning",
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size = "14 pages",
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abstract = "The performance of information retrieval systems
(IRSs) is usually measured using two different
criteria, precision and recall. Precision is the ratio
of the relevant documents retrieved by the IRS in
response to a user's query to the total number of
documents retrieved, whilst recall is the ratio of the
number of relevant documents retrieved to the total
number of relevant documents for the user's query that
exist in the documentary database. In fuzzy ordinal
linguistic IRSs (FOLIRSs), where extended Boolean
queries are used, defining the user's queries in a
manual way is usually a complex task. In this
contribution, our interest is focused on the automatic
learning of extended Boolean queries in FOLIRSs by
means of multi-objective evolutionary algorithms
considering both mentioned performance criteria. We
present an analysis of two well-known general-purpose
multi-objective evolutionary algorithms to learn
extended Boolean queries in FOLIRSs. These evolutionary
algorithms are the non-dominated sorting genetic
algorithm (NSGA-II) and the strength Pareto
evolutionary algorithm (SPEA2).",
- }
Genetic Programming entries for
Antonio Gabriel Lopez Herrera
Enrique Herrera Viedma
Francisco Herrera
Citations