Advertisement

Neurogenetic Approach for Solving Dynamic Programming Problems

  • Matheus Giovanni Pires
  • Ivan Nunes da Silva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6114)

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.

Keywords

Dynamic programming genetic algorithms Hopfield network 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hopfield, J.J., Tank, D.W.: Neural Computation of Decisions in Optimization Problems. Biological Cybernetics 52, 141–152 (1985)zbMATHMathSciNetGoogle Scholar
  2. 2.
    Zak, S.H., Upatising, V., Hui, S.: Solving Linear Programming Problems with Neural Networks. IEEE Trans. on Neural Networks. 6, 94–104 (1995)CrossRefGoogle Scholar
  3. 3.
    Xia, Y., Feng, G.: A New Neural Network for Solving Nonlinear Projection Equations. Neural Networks 20, 577–589 (2007)zbMATHCrossRefGoogle Scholar
  4. 4.
    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)zbMATHGoogle Scholar
  5. 5.
    Hillier, F.S., Lieberman, G.J.: Introduction to Operations Research. Holden Day, San Francisco (1980)Google Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    Graham, A.: Kronecher Products and Matrix Calculus. Ellis Horwood Ltd., Chichester (1981)Google Scholar
  8. 8.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Massachusetts (1996)Google Scholar
  9. 9.
    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)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Matheus Giovanni Pires
    • 1
    • 2
  • Ivan Nunes da Silva
    • 1
    • 2
  1. 1.Department of Computer EngineeringState University of Feira de SantanaFeira de SantanaBrazil
  2. 2.Department of Electrical EngineeringUniversity of São PauloSão CarlosBrazil

Personalised recommendations