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  • × author_ss:"Herrera, F."
  • × author_ss:"López-Herrera, A.G."
  1. López-Herrera, A.G.; Herrera-Viedma, E.; Herrera, F.: ¬A study of the use of multi-objective evolutionary algorithms to learn Boolean queries : a comparative study (2009) 0.00 
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    Abstract
    In this article, our interest is focused on the automatic learning of Boolean queries in information retrieval systems (IRSs) by means of multi-objective evolutionary algorithms considering the classic performance criteria, precision and recall. We present a comparative study of four well-known, general-purpose, multi-objective evolutionary algorithms to learn Boolean queries in IRSs. These evolutionary algorithms are the Nondominated Sorting Genetic Algorithm (NSGA-II), the first version of the Strength Pareto Evolutionary Algorithm (SPEA), the second version of SPEA (SPEA2), and the Multi-Objective Genetic Algorithm (MOGA).
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.6, S.1192-1207
  2. Cobo, M.J.; López-Herrera, A.G.; Herrera-Viedma, E.; Herrera, F.: Science mapping software tools : review, analysis, and cooperative study among tools (2011) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.7, S.1382-1402
  3. Cobo, M.J.; López-Herrera, A.G.; Herrera-Viedma, E.; Herrera, F.: SciMAT: A new science mapping analysis software tool (2012) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.8, S.1609-1630

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