Mostrar el registro sencillo del ítem

dc.contributor.author
Yannibelli, Virginia Daniela  
dc.contributor.author
Amandi, Analia Adriana  
dc.date.available
2018-11-21T18:18:02Z  
dc.date.issued
2017-10  
dc.identifier.citation
Yannibelli, Virginia Daniela; Amandi, Analia Adriana; A hybrid evolutionary algorithm based on adaptive mutation and crossover for collaborative learning team formation in higher education; Springer; Lecture Notes in Computer Science; 10585 LNCS; 10-2017; 345-354  
dc.identifier.issn
0302-9743  
dc.identifier.uri
http://hdl.handle.net/11336/64875  
dc.description.abstract
In this paper, we address a collaborative learning team formation problem in higher education environments. This problem considers a grouping criterion successfully evaluated in a wide variety of higher education courses and training programs. To solve the problem, we propose a hybrid evolutionary algorithm based on adaptive mutation and crossover processes. The behavior of these processes is adaptive according to the diversity of the evolutionary algorithm population. These processes are meant to enhance the evolutionary search. The performance of the hybrid evolutionary algorithm is evaluated on ten different data sets, and then, is compared with that of the best algorithm previously proposed in the literature for the addressed problem. The obtained results indicate that the hybrid evolutionary algorithm considerably outperforms the previous algorithm.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Springer  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
Adaptive Evolutionary Algorithms  
dc.subject
Collaborative Learning  
dc.subject
Collaborative Learning Team Formation  
dc.subject
Evolutionary Algorithms  
dc.subject
Hybrid Evolutionary Algorithms  
dc.subject
Simulated Annealing Algorithms  
dc.subject
Team Roles  
dc.subject.classification
Ciencias de la Computación  
dc.subject.classification
Ciencias de la Computación e Información  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
A hybrid evolutionary algorithm based on adaptive mutation and crossover for collaborative learning team formation in higher education  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2018-09-05T16:23:47Z  
dc.journal.volume
10585 LNCS  
dc.journal.pagination
345-354  
dc.journal.pais
Alemania  
dc.journal.ciudad
Heidelberg  
dc.description.fil
Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina  
dc.description.fil
Fil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina  
dc.journal.title
Lecture Notes in Computer Science  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://dx.doi.org/10.1007/978-3-319-68935-7_38  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/chapter/10.1007/978-3-319-68935-7_38