Neural Network for Optimization and Combinatorics
Neural Network for Optimization and Combinatorics
An Efficient Multivalued Hopfield Network for the Traveling Salesman Problem
Neural Processing Letters
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Neural implementation of Dijkstra's algorithm
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Graph partitioning via recurrent multivalued neural networks
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Design and analysis of maximum Hopfield networks
IEEE Transactions on Neural Networks
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The aim of this paper is to present the stochastic version of the multivalued neural model MREM, which has achieved very good results in many applications, as an optimization technique. The purpose of this stochastic version is to avoid certain local minima of the objective function minimized by the network, that is, the energy function. To this end, the description of the theoretical bases of this model, guaranteeing the convergence to minima, is carried out rigorously. In order to show the efficiency of this new model, the model, in its two versions, deterministic and stochastic, has been applied to the resolution of the well-known problem of graph partition, MaxCut. Computational experiments show that in most cases the stochastic model achieves better results than the deterministic one.