Large Scale Simulations of a Neural Network Model for the Graph Bisection Problem on Geometrically Connected Graphs

Hernández G.; Salinas, L.

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