COMBINATORIAL OPTIMIZATION WITH FEEDBACK ARTIFICIAL NEURAL NETWORKS Carsten Peterson Abstract: A brief review is given for using feedback artificial neural networks (ANN) to obtain good approximate solutions to combinatorial optimization problems. The key element is the mean field approximation (MFT) The methodology, which is illustrated for the graph bisection and knapsack problems, is easily generalized to Potts systems. The latter is related to the deformable templates method, which is illustrated with the track finding problem. MFT is based on a variational principle, which also can be generalized to non-integer problems. Proceedings of ICANN '95 International Conference on Artificial Neural Networks, October 1995, Paris, France , eds. F. Fogelman-Soulie and P. Gallinari, EC2 \& Cie (Paris 1995).