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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2010: Artifical Intelligence and Soft Computing pp 72–79Cite as

Neurogenetic Approach for Solving Dynamic Programming Problems

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Part of the book series: Lecture Notes in Computer Science ((LNAI,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.

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References

  1. Hopfield, J.J., Tank, D.W.: Neural Computation of Decisions in Optimization Problems. Biological Cybernetics 52, 141–152 (1985)

    MATH  MathSciNet  Google Scholar 

  2. Zak, S.H., Upatising, V., Hui, S.: Solving Linear Programming Problems with Neural Networks. IEEE Trans. on Neural Networks. 6, 94–104 (1995)

    Article  Google Scholar 

  3. Xia, Y., Feng, G.: A New Neural Network for Solving Nonlinear Projection Equations. Neural Networks 20, 577–589 (2007)

    Article  MATH  Google Scholar 

  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)

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  5. Hillier, F.S., Lieberman, G.J.: Introduction to Operations Research. Holden Day, San Francisco (1980)

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  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)

    Article  Google Scholar 

  7. Graham, A.: Kronecher Products and Matrix Calculus. Ellis Horwood Ltd., Chichester (1981)

    Google Scholar 

  8. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Massachusetts (1996)

    Google Scholar 

  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)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, State University of Feira de Santana, CEP 44031-460, Feira de Santana, BA, Brazil

    Matheus Giovanni Pires & Ivan Nunes da Silva

  2. Department of Electrical Engineering, University of São Paulo, CP 359, CEP 13566.590, São Carlos, SP, Brazil

    Matheus Giovanni Pires & Ivan Nunes da Silva

Authors
  1. Matheus Giovanni Pires
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  2. Ivan Nunes da Silva
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Academy of Humanities and Economics in Łódź, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Rafał Scherer

  3. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley Initiative in Soft Computing (BISC), 94720-1776, CA

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory Department of Electrical and Computer Engineering, University of Louisville, 40292, Louisville, KY

    Jacek M. Zurada

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© 2010 Springer-Verlag Berlin Heidelberg

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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

  • Online ISBN: 978-3-642-13232-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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