Abstract
Dynamic programming has provided a powerful approach to solve optimization problems, but its applicability has sometimes been limited because of the high computational effort required by the conventional algorithms. This paper presents an association between Hopfield networks and genetic algorithms, which cover extensive search spaces and guarantee the convergence of the system to the equilibrium points that represent feasible solutions for dynamic programming problems. © 2010 Springer-Verlag.
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CITATION STYLE
Pires, M. G., & Da Silva, I. N. (2010). Neurogenetic approach for solving dynamic programming problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6114 LNAI, pp. 72–79). https://doi.org/10.1007/978-3-642-13232-2_10
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