Neuro-genetic approach for solving constrained nonlinear optimization problems

Authors:
Fabiana Cristina Bertoni;Ivan Nunes da Silva
Affiliations:
University of São Paulo, Department of Electrical Engineering, São Carlos, SP, Brazil;University of São Paulo, Department of Electrical Engineering, São Carlos, SP, Brazil
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

Citing 4
Cited 0

Nonlinear systems analysis (2nd ed.)

Nonlinear systems analysis (2nd ed.)
An introduction to genetic algorithms

An introduction to genetic algorithms
Multilayer perceptron for nonlinear programming

Computers and Operations Research
A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a neuro-genetic approach for solving constrained nonlinear optimization problems. Genetic algorithm must its popularity to make possible cover nonlinear and extensive search spaces. On the other hand, artificial neural networks have high computational 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. 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 constrained nonlinear optimization problems. Simulated examples are presented to demonstrate that proposed method provides a significant improvement.