Simulated annealing: theory and applications
Simulated annealing: theory and applications
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Distributed Algorithm for Max Independent Set Problem Based on Hopfield Networks
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Discrete Applied Mathematics
FPGA implementation of an adaptive stochastic neural model
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
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The ability to map and solve combinatorial optimization problems with constraints on neural networks has frequently motivated a proposal for using such a model of computation. We introduce a new stochastic neural model, working out for a specific class of constraints, which is able to choose adaptively its weights in order to find solutions into a proper subspace (feasible region) of the search space. We show its asymptotic convergence properties and give evidence of its ability to find hight quality solution on benchmark and randomly generated instances of a specific problem.