Applying multi-objective evolutionary algorithms to the automatic learning of extended Boolean queries in fuzzy ordinal linguistic information retrieval systems

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{LopezHerrera20092192,
  author =       "A. G. Lopez-Herrera and E. Herrera-Viedma and 
                 F. Herrera",
  title =        "Applying multi-objective evolutionary algorithms to
                 the automatic learning of extended {Boolean} queries in
                 fuzzy ordinal linguistic information retrieval
                 systems",
  journal =      "Fuzzy Sets and Systems",
  volume =       "160",
  number =       "15",
  pages =        "2192--2205",
  year =         "2009",
  note =         "Special Issue: The Application of Fuzzy Logic and Soft
                 Computing in Information Management",
  ISSN =         "0165-0114",
  DOI =          "doi:10.1016/j.fss.2009.02.013",
  URL =          "http://www.sciencedirect.com/science/article/B6V05-4VPM59B-4/2/21a5a32bf1a659a371ce5c4d320da182",
  keywords =     "genetic algorithms, genetic programming, MOGP,
                 Information retrieval systems, Inductive query by
                 example, Multi-objective evolutionary algorithms, Query
                 learning",
  size =         "14 pages",
  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