Paper Info
Title
A study of the use of multi-objective evolutionary algorithms to learn Boolean queries: A comparative study
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.
Year
DOI
Venue
2009
10.1002/asi.v60:6
JASIST
Keywords
Field
DocType
classic performance criterion,automatic learning,comparative study,multi-objective genetic algorithm,wiley periodicals,genetic algorithm,multi-objective evolutionary algorithm,evolutionary algorithm,strength pareto evolutionary algorithm,boolean query
Evolutionary algorithm,Computer science,Spea,Precision and recall,Sorting,Boolean algebra,Artificial intelligence,Evolutionary programming,Genetic algorithm,Pareto principle
Journal
Volume
Issue
ISSN
60
6
1532-2882
Citations 
PageRank 
References 
7
0.40
17
Authors
3
Name
Order
Citations
PageRank
Antonio Gabriel López-herrera142318.65
Enrique Herrera-Viedma213105642.24
Francisco Herrera3273911168.49