Abstract
While looking at aging of individuals, we take for granted that one of aging reasons is related with individual’s ability to learn rapidly, to adapt to sudden environmental changes and survive. We explain maturation and aging of standard non-linear single layer perceptron by increasing of components of the weight vector and dramatic decline of a gradient. We analyze also artificial immune system trained by the mutation based genetic learning algorithm. In both, the connectionist and genetic learning, we obtain saturation and an inverted letter “U” shape dependence between success in learning and the “age”.
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References
Kirkwood, T.B.L., Austad, S.: Why do we age? Nature 408, 233–237 (2000)
Gavrilov, L., Gavrilova, N.: The reliability theory of aging and longevity. J. of Theoretical Biology 213, 527–545 (2001)
Farmer, J.D., Packard, N.H., Perelson, A.S.: The immune system, adaptation and machine learning. Physica 22D, 182–204 (1986)
De Castro, L.N., Timmis, J.: Artificial Immune Systems: A new computational approach. Springer, London (2002)
Thomsen, O., Kettel, K.: Die Stärke der menschlichen Isoagglutinine und entsprechende Blutkörperchenreceptoren in verschiedenen Lebensaltern. Z. Immunitatsforsch. 63, 67–93 (1927)
Makinodan, T., Yunis, E.: Immunololgy and Aging. Plenum Medical Book Company, London (1977)
Alvager, T., French, V., Putman, G., Herrmann, D., Anderson, E., Schnitzer, S.: An artificial neural network model of aging and cognition. In: Proc. Artificial Intelligence and Applications, pp. 403–440 (2003)
Cerella, J.: Aging on a neural network. In: Proc. Int. Joint Conf. on Neural Networks, p. 601 (1989)
Raudys, S.: An adaptation model for simulation of aging process. Int. J. of Modern Physics C 13, 1075–1086 (2002)
Penna, T.J.P.: A bit-string model for biological aging. J. of Statistical Physics 78, 1629–1633 (1995)
Pletcher, S.D., Neuhauser, C.: Biological aging ( criteria for modeling and a new mechanistic model. Int.l J. Modern Physics C 11, 525–546 (2000)
Huang, Z.F., Stauffer, D.: Stochastic Penna model for biological aging. Theory in Biociences 120, 21–28 (2001)
Biecek, P., Cebrat, S.: Immunity in the noisy Penna model. Int. J. Modern Physics C 17(12), 1823–1829 (2006)
Haykin, S.: Neural Networks: A comprehensive foundation, 2nd edn. Prentice Hall, Upper Saddle River (1999)
Raudys, S.: Statistical and Neural Classifiers: An integrated approach to design. Springer, London (2001)
Raudys, S., Pumputis, A.: Group interests of agents functioning in changing environments. In: Pěchouček, M., Petta, P., Varga, L.Z. (eds.) CEEMAS 2005. LNCS (LNAI), vol. 3690, pp. 559–563. Springer, Heidelberg (2005)
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Raudys, S. (2007). Aging in Artificial Learning Systems. In: Almeida e Costa, F., Rocha, L.M., Costa, E., Harvey, I., Coutinho, A. (eds) Advances in Artificial Life. ECAL 2007. Lecture Notes in Computer Science(), vol 4648. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74913-4_27
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DOI: https://doi.org/10.1007/978-3-540-74913-4_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74912-7
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