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Memorias de investigación
Artículos en revistas:
Network measures for information extraction in evolutionary algorithms
Año:2013
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
Problem domain information extraction is a critical issue in many real-world optimization problems. Increasing the repertoire of techniques available in evolutionary algorithms with this purpose is fundamental for extending the applicability of these algorithms. In this paper we introduce a unifying information mining approach for evolutionary algorithms. Our proposal is based on a division of the stages where structural modelling of the variables interactions is applied. Particular topological characteristics induced from different stages of the modelling process are identified. Network theory is used to harvest problem structural information from the learned probabilistic graphical models (PGMs). We show how different statistical measures, previously studied for networks from different domains, can be applied to mine the graphical component of PGMs. We provide evidence that the computed measures can be employed for studying problem difficulty, classifying different problem instances and predicting the algorithm behavior.
Internacional
Si
JCR del ISI
Si
Título de la revista
International Journal of Computational Intelligence Systems
ISSN
1875-6883
Factor de impacto JCR
1,471
Información de impacto
Volumen
DOI
Número de revista
Desde la página
1163
Hasta la página
1188
Mes
SIN MES
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Roberto Santana (University of the Basque Country)
  • Autor: Ruben Armañanzas Arnedillo (UPM)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
  • Departamento: Inteligencia Artificial
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