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Evolutionary Computation and Evolving Connectionist Systems

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Evolving Connectionist Systems

Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

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Abstract

Nature’s diversity of species is tremendous. How does mankind evolve in the enormous variety of variants — in other words, how does nature solve the optimisation problem of perfecting mankind? One answer to this question may be found in Charles Darwin’s theory of evolution. Evolution is concerned with the development of generations of populations of individuals governed by fitness criteria. But this process is much more complex, as individuals, in addition to what Nature has defined for them, develop in their own way — they learn and evolve during their lifetime.

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Further Reading

  • Kasabov, N. (1999) Evolving connectionist systems and applications for adaptive speech recognition. Proc. IJCNN’99, Washington DC. IEEE Press.

    Google Scholar 

  • Goldberg, D. E. (1989) Genetic Algorithms in Search, Optimisation and Machine Learning. Addison-Wesley, Reading.

    Google Scholar 

  • Michaliewicz, Z. (1992) Genetic Algorithms i- Data Structures = Evolutionary Programs. Springer-Verlag, Berlin.

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  • Koza, J. (1992) Genetic Programming. MIT Press, Cambridge, MA.

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  • Machado, R. J. and da Rocha, A. F. (1992) Evolutive fuzzy neural networks. Proc. First IEEE Conference on Fuzzy Systems, pp. 493–499.

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  • Fogel, D., Fogel, L. and Porto, V. (1990) Evolving neural networks. Biological Cybernetics, 63, 487–493.

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  • Yao, X. (1993) Evolutionary artificial neural networks. International Journal of Neural Systems, 4 (3), 203–222.

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  • Watts, M. and Kasabov, N. (1998) Genetic algorithms for the design of fuzzy neural networks. Proc. ICONIP’98 - The Fifth International Conference on Neural Information Processing, Kitakyushu, Japan (eds. S. Usui and T. Omori). IOS Press, Vol. 2, pp. 793–796.

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  • Whitley, D. and Bogart, C. (1990) The evolution of connectivity: pruning neural networks using genetic algorithms. Proc. International Joint Conference on Neural Networks, pp. 17–22.

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  • Edelman, G. (1992) Neuronal Darwinism: The Theory of Neuronal Group Selection. Basic Books, New York.

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  • Furuhashi, T., Nakaoka, K. and Uchikawa, Y. (1994) A new approach to genetic based machine learning and an efficient finding of fuzzy rules. Proc. WWW’94 Workshop, University of Nagoya, Japan, pp. 114–122.

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  • Adami, C. (1998) Introduction to Artificial Life. Springer-Verlag, London.

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© 2003 Springer-Verlag London

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Kasabov, N. (2003). Evolutionary Computation and Evolving Connectionist Systems. In: Evolving Connectionist Systems. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-3740-5_6

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  • DOI: https://doi.org/10.1007/978-1-4471-3740-5_6

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-400-0

  • Online ISBN: 978-1-4471-3740-5

  • eBook Packages: Springer Book Archive

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