Large Scale Simulations of a Neural Network Model for the Graph Bisection Problem on Geometrically Connected Graphs
Abstract
In this work some preliminary numerical results obtained by large scale simulations of the sequential dynamics of a neural network model for the graph bisection problem on random geometrically connected graphs are presented. It can be concluded that the sequential dynamic is a low cost, effective and very fast local minima optimization heuristic for the Graph Bisection Problem. © 2005 Elsevier B.V. All rights reserved.
Más información
Título según SCOPUS: | Large Scale Simulations of a Neural Network Model for the Graph Bisection Problem on Geometrically Connected Graphs |
Título de la Revista: | Electronic Notes in Discrete Mathematics |
Volumen: | 18 |
Editorial: | Elsevier |
Fecha de publicación: | 2004 |
Página de inicio: | 151 |
Página final: | 156 |
Idioma: | English |
URL: | http://www.scopus.com/inward/record.url?eid=2-s2.0-33947502169&partnerID=q2rCbXpz |
DOI: |
10.1016/j.endm.2004.06.024 |
Notas: | SCOPUS |