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Neuro-genetic Approach for Solving Constrained Nonlinear Optimization Problems

  • Fabiana Cristina Bertoni
  • Ivan Nunes da Silva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

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.

Keywords

Genetic Algorithm Equilibrium Point Nonlinear Optimization Inequality Constraint Recurrent Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fabiana Cristina Bertoni
    • 1
  • Ivan Nunes da Silva
    • 1
  1. 1.Department of Electrical EngineeringUniversity of São PauloSão CarlosBrazil

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