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Memorias de investigación
Ponencias en congresos:
Automatically Modeling Hybrid Evolutionary Algorithms from Past Executions
Año:2010
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
The selection of the most appropriate Evolutionary Algorithm for a given optimization problem is a difficult task. Hybrid Evolutionary Algorithms are a promising alternative to deal with this problem. By means of the combination of different heuristic optimization approaches, it is possible to profit from the benefits of the best approach, avoiding the limitations of the others. Nowadays, there is an active research in the design of dynamic or adaptive hybrid algorithms. However, little research has been done in the automatic learning of the best hybridization strategy. This paper proposes a mechanism to learn a strategy based on the analysis of the results from past executions. The proposed algorithm has been evaluated on a well-known benchmark on continuous optimization. The obtained results suggest that the proposed approach is able to learn very promising hybridization strategies.
Internacional
Si
Nombre congreso
EvoApplications 2010
Tipo de participación
960
Lugar del congreso
Revisores
Si
ISBN o ISSN
978-3-642-12238-5
DOI
Fecha inicio congreso
07/04/2010
Fecha fin congreso
09/04/2010
Desde la página
422
Hasta la página
431
Título de las actas
Automatically Modeling Hybrid Evolutionary Algorithms from Past Executions
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Santiago Muelas Pascual (UPM)
  • Autor: Jose Maria Peña Sanchez (UPM)
  • Autor: Antonio Latorre De la Fuente (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
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