%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 }