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Paper details
Number 3 - September 1997
Volume 7 - 1997
Genetic algorithms and Hopfield neural networks for solving combinatorial problems
Jerzy Balicki, Andrzej Stateczny, Bogdan Żak
Abstract
In this paper, a Hopfield neural-network population for solving NP-hard multiobjective optimization problems with zero-one decision variables is proposed. The initial states of the Hopfield models in this population are modified by a genetic algorithm and the energy functions are constructed by using a non-negative convex combination method. Accordingly, optimization neural networks for the related optimization problem are designed. Simulation results are also presented to illustrate the effectiveness of the approach.
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