DocumentCode :
3115457
Title :
Neurogenetic approach for solving dynamic programming problems
Author :
Pires, Matheus Giovanni ; Da Silva, Ivan Nunes ; Bertoni, Fabiana Cristina
Author_Institution :
Dept. of Electr. Eng., Univ. of Sao Paulo, Sao Carlos
fYear :
2008
fDate :
12-15 Oct. 2008
Firstpage :
2144
Lastpage :
2149
Abstract :
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.
Keywords :
combinatorial mathematics; dynamic programming; genetic algorithms; search problems; combinatorial optimization problem; dynamic programming problems; genetic algorithm; modified Hopfield neural network; neurogenetic approach; search spaces; shortest path problem; Artificial neural networks; Constraint optimization; Design optimization; Dynamic programming; Equations; Genetic algorithms; Hopfield neural networks; Neurofeedback; Neurons; Shortest path problem; Hopfield neural network; genetic algorithm; shortest path problem;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 2008. SMC 2008. IEEE International Conference on
Conference_Location :
Singapore
ISSN :
1062-922X
Print_ISBN :
978-1-4244-2383-5
Electronic_ISBN :
1062-922X
Type :
conf
DOI :
10.1109/ICSMC.2008.4811609
Filename :
4811609
Link To Document :
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