Evolutionary Learning of Boolean Queries by Multiobjective Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{cordon:ppsn2002:pp710,
-
author = "Oscar Cordon and Enrique Herrera-Viedma and
Maria Luque",
-
title = "Evolutionary Learning of {Boolean} Queries by
Multiobjective Genetic Programming",
-
booktitle = "Parallel Problem Solving from Nature - PPSN VII",
-
address = "Granada, Spain",
-
month = "7-11 " # sep,
-
pages = "710--719",
-
year = "2002",
-
editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and
Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and
Hans-Paul Schwefel",
-
number = "2439",
-
series = "Lecture Notes in Computer Science, LNCS",
-
publisher = "Springer-Verlag",
-
keywords = "genetic algorithms, genetic programming, MOGA, Pattern
recognition and classification/datamining,Web services,
Multi-objective",
-
ISBN = "3-540-44139-5",
-
DOI = "doi:10.1007/3-540-45712-7_68",
-
abstract = "The performance of an information retrieval system is
usually measured in terms of two different criteria,
precision and recall. This way, the optimisation of any
of its components is a clear example of a
multiobjective problem. However, although evolutionary
algorithms have been widely applied in the information
retrieval area, in all of these applications both
criteria have been combined in a single scalar fitness
function by means of a weighting scheme. In this paper,
we will tackle with a usual information retrieval
problem, the automatic derivation of Boolean queries,
by incorporating a well known Pareto-based
multiobjective evolutionary approach, MOGA, into a
previous proposal of a genetic programming technique
for this task.",
- }
Genetic Programming entries for
Oscar Cordon
Enrique Herrera Viedma
Maria Luque Rodriguez
Citations