A study of the use of multi-objective evolutionary algorithms to learn Boolean queries: A comparative study

Authors:
A. G. López-Herrera;E. Herrera-Viedma;F. Herrera
Affiliations:
Department of Computer Sciences and A.I., University of Granada, E-18071-Granada, Spain;Department of Computer Sciences and A.I., University of Granada, E-18071-Granada, Spain;Department of Computer Sciences and A.I., University of Granada, E-18071-Granada, Spain
Venue:
Journal of the American Society for Information Science and Technology
Year:
2009

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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). © 2009 Wiley Periodicals, Inc.