Skip to Main content Skip to Navigation
Journal articles

Adaptive genetic algorithms guided by decomposition for PCSPs: application to frequency assignment problems

Abstract : This paper proposes Adaptive Genetic Algorithms Guided by structural knowledges coming from decomposition methods, for solving PCSPs. The family of algorithms called AGAGD_x_y is designed to be doubly generic, meaning that any decomposition method and different heuristics for the genetic operators can be considered. To validate the approach, the decomposition algorithm due to Newman was used and several crossover operators based on structural knowledge such as the cluster, separator and the cut were tested. The experimental results obtained on the most challenging Minimum Interference-FAP problems of CALMA instances are very promising and lead to interesting perspectives to be explored in the future.
Document type :
Journal articles
Complete list of metadatas

https://hal.univ-lorraine.fr/hal-01784134
Contributor : Lcoms Ul <>
Submitted on : Thursday, May 3, 2018 - 9:30:20 AM
Last modification on : Monday, December 2, 2019 - 1:52:02 PM

Identifiers

Collections

Citation

Lamia Sadeg-Belkacem, Zineb Habbas, Wassila Aggoune-Mtalaa. Adaptive genetic algorithms guided by decomposition for PCSPs: application to frequency assignment problems. Frontiers of Computer Science, Springer Verlag, 2016, 10 (6), pp.1012 - 1025. ⟨10.1007/s11704-016-4552-4⟩. ⟨hal-01784134⟩

Share

Metrics

Record views

101