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Random perturbations to Hebbian synapses of associative memory using a genetic algorithm

  • Plasticity Phenomena (Maturing, Learning and Memory)
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Biological and Artificial Computation: From Neuroscience to Technology (IWANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1240))

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Abstract

We apply evolutionary algorithms to Hopfield model of associative memory. Previously we reported that a genetic algorithm using ternary chromosomes evolves the Hebb-rule associative memory to enhance its storage capacity by pruning some connections. This paper describes a genetic algorithm using real-encoded chromosomes which successfully evolves over-loaded Hebbian synaptic weights to function as an associative memory. The goal of this study is to shed new light on the analysis of the Hopfield model, which also enables us to use the model as more challenging test suite for evolutionary computations.

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References

  1. K. DeJong (1975) An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Ph.D thesis, University of Michigan.

    Google Scholar 

  2. D. Ackley (1987) A Connectionist Machine for Genetic Hillclimbing. Boston: Kluwer.

    Google Scholar 

  3. K. Deb (1989) Genetic Algorithms in Multimodal Function Optimization. Master's thesis. The University of Alabama.

    Google Scholar 

  4. H. Mühlenbein, M. Schomish, and J. Born (1991) The Parallel Genetic Algorithm as Function Optimizer. Parallel Computing 17 pp619–632.

    Google Scholar 

  5. J. D. Schaffer, B. A. Caruana, L. J. Eshlman, and R. Das (1991) A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization. Proceedings of the 3rd International Conference on Genetic Algorithms. pp51–60.

    Google Scholar 

  6. D. Whitley, K. Mathias, S. Rana, and J. Dzubera (1995) Building Better Test Functions. Proceedings of the 6th International Conference on Genetic Algorithms. pp239–247.

    Google Scholar 

  7. S. M. Mahfoud (1995) A. Comparison of Parallel and Sequential Niching Methods. Proceedings of the 6th International Conference on Genetic Algorithms. pp239–247.

    Google Scholar 

  8. D. Schlierkamp-Voosen, and H. Mühlenbein (1996) Adaptation of Population Sizes by Competing Subpopulation. Proceedings of IEEE International Conference on Evolutionary Computation. pp330–335.

    Google Scholar 

  9. H. Mühlenbein, and D. Schlierkamp-Voosen (1995) Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization. Evolutionary Computation 1 (1996) pp25–49.

    Google Scholar 

  10. E. Gardner (1988) The Phase Space of Interactions in Neural Network Models. J. Phys A: Math Gen. 21. pp257–270.

    Google Scholar 

  11. H. Sompolinsky (1986) Neural Network with Non-linear Synapses and Static Noise. Phys. Rev. A34 pp2571–2574.

    Google Scholar 

  12. J. D. Schaffer, D. Whitley, and L. J. Eshelman (1992) Combinations of Genetic Algorithms and Neural Networks: A Survey of the State of the Art. Proceedings of the Workshop on Combinations of Genetic Algorithms and Neural Networks. pp1–37.

    Google Scholar 

  13. X. Yao (1993) A Review of Evolutionary Artificial Neural Networks. International Journal of Intelligent Systems 8. pp539–567.

    Google Scholar 

  14. A. Imada, and K. Araki (1997) Evolved Asymmetry and Dilution of Random Synaptic Weights in Hopfield Network Turn a Spin-glass Phase into Associative Memory. The 2nd International Conference on Computational Intelligence and Neuroscience (to appear)

    Google Scholar 

  15. A. Imada, and K. Araki (1995) Genetic Algorithm Enlarges the Capacity of Associative Memory. Proceedings of the 6th International Conference on Genetic Algorithms. pp413–420.

    Google Scholar 

  16. J. Komlós, and R. Paturi (1988) Convergence Results in an Associative Memory Model. Neural Networks 1. pp239–250.

    Google Scholar 

  17. J. J. Hopfield (1982) Neural Networks and Physical Systems with Emergent Collective Computational Abilities. Proceedings of the National Academy of Sciences, USA. 79. pp2554–2558.

    Google Scholar 

  18. D. O. Hebb (1949) The Organization of Behavior. Wiley.

    Google Scholar 

  19. D. J. Amit, H. Gutfreund, and H. Sompolinsky (1985) Statistical Mechanics of Neural Networks near Saturation Annals. of Physics 173, pp30–67.

    Google Scholar 

  20. R. J. McEliced, E. C. Posner, E. R. Rodemick, and S. S. Venkatesh (1987) The Capacity of the Hopfield Associative Memory. IEEE Trans. Information Theory IT-33. pp461–482.

    Google Scholar 

  21. T. Kohonen, and M. Ruohonen (1973) Representation of Associated Data by Matrix Operators. IEEE Trans. Computers C-22(7). pp701–702.

    Google Scholar 

  22. G. Pancha, S. S. VenKatesh (1993) Feature and Memory-Selective Error correction in Neural Associative Memory. in M. H. Hassoun eds. Associative Neural Memories: Theory and Implementation. Oxford University Press. pp225–238.

    Google Scholar 

  23. G. Syswerda (1989) Uniform Crossover in Genetic Algorithms. Proceedings of the 3rd International Conference on Genetic Algorithms, 2.

    Google Scholar 

  24. D. J. Amit, H. Gutfreund, and H. Sompolinsky (1985) Storing Infinite Number of Patterns in a Spin-glass Model of Neural Networks. Physical Review Letters 55. pp1530–1533.

    Google Scholar 

  25. J. A. Hertz, G. Grinstein, and S. A. Solla (1987) Irreversible Spin Glasses an Neural Networks. in L. N. van Hemmen and I. Morgenstern eds. Heidelberg Colloquium on Glassy Dynamics. Lecture Notes in Physics No.275 Springer-Verlag, pp538–546.

    Google Scholar 

  26. G. Parisi (1986) Asymmetric Neural Networks and the Process of Learning. J. Phys. A19 ppL675–L680.

    Google Scholar 

  27. W. Krauth, J.-P. Nadal, and M.Mezard (1988) The Roles of Stability and Symmetry in the Dynamics of Neural Networks. J. Phys. A: Math. Gen. 21 pp2995–3011.

    Google Scholar 

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Authors and Affiliations

  1. Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, 630-01, Nara, Japan

    Akira Imada

  2. Department of Computer Science and Computer Engineering Graduate School of Information Science and Electrical Engineering, Kyusyu University, 6-1 Kasuga-koen, Kasuga, 816, Fukuoka, Japan

    Keijiro Araki

Authors
  1. Akira Imada
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  2. Keijiro Araki
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Editor information

José Mira Roberto Moreno-Díaz Joan Cabestany

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© 1997 Springer-Verlag Berlin Heidelberg

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Imada, A., Araki, K. (1997). Random perturbations to Hebbian synapses of associative memory using a genetic algorithm. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds) Biological and Artificial Computation: From Neuroscience to Technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032498

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  • DOI: https://doi.org/10.1007/BFb0032498

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63047-0

  • Online ISBN: 978-3-540-69074-0

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