An energy function for the random neural network

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 Monte Carlo simulation during the search for solutions. In this paper, we propose a general energy function for a new neural model, the random neural model of Gelenbe. This model proposes a scheme of interaction between the neurons and not a dynamic equation of the system. Then, we apply this general energy function to different optimization problems.