Genetic algorithms and Hopfield neural networks for solving combinatorial problems - Balicki, Jerzy; Stateczny, Andrzej; Żak, Bogdan (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: sterowanie; sterowanie-teoria; sztuczna inteligencja; matematyka stosowana; informatyka