Using the general energy function of the random neural networks to solve the graph partitioning problem

Typically, the neural networks are used to provide heuristic solutions to very difficult optimization problems. This is usually achieved by designing neural networks whose energy function mimics a cost function which embodies the optimization problem to be solved. In this paper, we propose to use a general energy function of the random neural network to solve the graph partitioning problem. We show as this energy function permits to define a general method to use the random neural network in the resolution of combinatorial optimization problems.