Neurogenetic approach for solving dynamic programming problems

The shortest path problem is the classical combinatorial optimization problem arising in numerous planning and designing contexts. This paper presents a association of a modified Hopfield neural network, which is a computing model capable of solving a large class of optimization problems, with a genetic algorithm, that to make possible cover nonlinear and extensive search spaces, which guarantees the convergence of the system to the equilibrium points that represent solutions for the dynamic optimization problems. Experimental results are presented and discussed.