Uma arquitetura neuro-genética para otimização não-linear restrita / Neuro-genetic architecture for constrained nonlinear optimization

AUTOR(ES)

Fabiana Cristina Bertoni

DATA DE PUBLICAÇÃO

2007

RESUMO

Systems based on artificial neural networks and genetic algorithms are an alternative method for solving systems optimization problems. The genetic algorithms must its popularity to make possible cover nonlinear and extensive search spaces. Artificial neural networks have high processing rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. Neural networks with feedback connections provide a computing model capable of solving a large class of optimization problems, which refer to optimization of an objective function that can be subject to constraints. This thesis presents a novel approach for solving constrained nonlinear optimization problems using a neuro-genetic approach. More specifically, a modified Hopfield neural network is associated with a genetic algorithm in order to guarantee the convergence of the network to the equilibrium points, which represent feasible solutions for the constraint nonlinear optimization problem.

ASSUNTO(S)

neural networks constrained nonlinear optimization otimização não-linear restrita redes neurais algoritmos genéticos genetic algorithms

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