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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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
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