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.
A discrete adaptive stochastic neural model for constrained optimization / G. Grossi - In: Artificial Neural Networks – ICANN 2006 / [a cura di] S. Kollias, A. Stafylopatis, W. Duch, E. Oja. - Berlin : Springer, 2006. - ISBN 9783540386254. - pp. 641-650 (( Intervento presentato al 16. convegno International Conference on Artificial Neural Networks - ICANN 2006 tenutosi a Atene nel 2006.
A discrete adaptive stochastic neural model for constrained optimization
G. GrossiPrimo
2006
Abstract
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.
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