General method to solve combinatorial optimization problems with random neural networks

Abstract:

Since Hopfield's seminal work on energy functions for neural networks and their consequence for the approximate solution of optimization problems, much attention has been devoted to neural heuristics for combinatorial optimization. These heuristics are often very time consuming, because of the need for randomization or Montecarlo simulation during the search for solutions. In this paper, we propose a fast general method to solve combinatorial optimization problems using the random neural model of Gelenbe. Then, we present the solution of the graph partitioning problem with this method.

Año de publicación:

1996

Keywords:

Fuente:

scopus