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.
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References
Hopfield, J.J., Tank, D.W.: Neural Computation of Decisions in Optimization Problems. Biological Cybernetics 52, 141–152 (1985)
Zak, S.H., Upatising, V., Hui, S.: Solving Linear Programming Problems with Neural Networks. IEEE Trans. on Neural Networks. 6, 94–104 (1995)
Xia, Y., Feng, G.: A New Neural Network for Solving Nonlinear Projection Equations. Neural Networks 20, 577–589 (2007)
Silva, I.N., Goedtel, A., Flauzino, R.A.: The Modified Hopfield Architecture Applied in Dynamic Programming Problems and Bipartite Graph Optimization. International Journal of Hybrid Intelligent Systems 4, 17–26 (2007)
Hillier, F.S., Lieberman, G.J.: Introduction to Operations Research. Holden Day, San Francisco (1980)
Aiyer, S.V.B., Niranjan, M., Fallside, F.: A Theoretical Investigation into the Performance of the Hopfield Network. IEEE Trans. on Neural Networks 1, 204–215 (1990)
Graham, A.: Kronecher Products and Matrix Calculus. Ellis Horwood Ltd., Chichester (1981)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Massachusetts (1996)
Chiu, C., Maa, C.Y., Shanblatt, M.A.: Energy Function Analysis of Dynamic Programming Neural Networks. IEEE Trans. on Neural Networks 2, 418–426 (1991)
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Pires, M.G., da Silva, I.N. (2010). Neurogenetic Approach for Solving Dynamic Programming Problems. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artifical Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13232-2_10
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DOI: https://doi.org/10.1007/978-3-642-13232-2_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13231-5
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