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.
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Bertoni, F.C., da Silva, I.N. (2006). Neuro-genetic Approach for Solving Constrained Nonlinear Optimization Problems. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_91
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DOI: https://doi.org/10.1007/11893295_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
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