Evolutionary Learning of Boolean Queries by
Multiobjective Genetic Programming
O. Cordon, E. Herrera-Viedma, and M. Luque. Parallel Problem Solving from Nature - PPSN VII, 2439, page 710--719. Granada, Spain, Springer-Verlag, (7-11 September 2002)
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.
%0 Conference Paper
%1 cordon:ppsn2002:pp710
%A Cordon, Oscar
%A Herrera-Viedma, Enrique
%A Luque, Maria
%B Parallel Problem Solving from Nature - PPSN VII
%C Granada, Spain
%D 2002
%E Merelo-Guervos, Juan J.
%E Adamidis, Panagiotis
%E Beyer, Hans-Georg
%E Fernandez-Villacanas, Jose-Luis
%E Schwefel, Hans-Paul
%I Springer-Verlag
%K MOGA, Multi-objective Pattern algorithms, and classification/datamining,Web genetic programming, recognition services,
%N 2439
%P 710--719
%T Evolutionary Learning of Boolean Queries by
Multiobjective Genetic Programming
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2439&spage=710
%X 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.
%Z Available from
http://link.springer.de/link/service/series/0558/papers/2439/243900710.pdf
%@ 3-540-44139-5
@inproceedings{cordon:ppsn2002:pp710,
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.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Granada, Spain},
annote = {Available from
http://link.springer.de/link/service/series/0558/papers/2439/243900710.pdf},
author = {Cordon, Oscar and Herrera-Viedma, Enrique and Luque, Maria},
biburl = {https://www.bibsonomy.org/bibtex/2426619af7f07ca7206818ffe49a951ae/brazovayeye},
booktitle = {Parallel Problem Solving from Nature - PPSN VII},
editor = {Merelo-Guervos, Juan J. and Adamidis, Panagiotis and Beyer, Hans-Georg and Fernandez-Villacanas, Jose-Luis and Schwefel, Hans-Paul},
interhash = {220a44a07754820768c54b490eb7fe83},
intrahash = {426619af7f07ca7206818ffe49a951ae},
isbn = {3-540-44139-5},
keywords = {MOGA, Multi-objective Pattern algorithms, and classification/datamining,Web genetic programming, recognition services,},
month = {7-11 September},
number = 2439,
pages = {710--719},
publisher = {Springer-Verlag},
series = {Lecture Notes in Computer Science, LNCS},
timestamp = {2008-06-19T17:38:06.000+0200},
title = {Evolutionary Learning of Boolean Queries by
Multiobjective Genetic Programming},
url = {http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2439&spage=710},
year = 2002
}