Hybrid Hopfield Architecture for Solving Nonlinear Programming Problems

This paper presents a neurogenetic approach for solving nonlinear programming problems. Genetic algorithm must its popularity to make possible cover nonlinear and extensive search spaces. Neural networks with feedback connections provide a computing model capable of solving a large class of optimization problems. The association of a modified Hopfield network with genetic algorithm guarantees the convergence of the system to the equilibrium points, which represent feasible solutions for nonlinear programming problems.

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